Effects of Vehicular Communication on Risk Assessment in ... - MDPI

applied sciences Article

Effects of Vehicular Communication on Risk Assessment in Automated Driving Vehicles Donghoon Shin 1 , Kangmun Park 2 1 2 3

*

and Manbok Park 3, *

Department of Mechanical Engineering, College of Engineering, Republic of Korea Naval Academy, Changwon-si 51704, Korea; [email protected] Department of Computer Science, College of Natural Science, Republic of Korea Naval Academy, Changwon-si 51704, Korea; [email protected] Department of Electronic Engineering, College of Convergence Technology, Korea National University of Transportation, Chungju-si 27469, Korea; [email protected] Correspondence: [email protected]; Tel.: +82-43-841-5369

Received: 6 December 2018; Accepted: 13 December 2018; Published: 15 December 2018

Featured Application: Vehicular communication is expected to overcome conventional in-vehicle sensor limitations such as ranging and accuracy. In addition, this will enhance the perception and decision performance of automated driving vehicle in terms of guaranteed safety. Abstract: This paper proposes a human-centered risk assessment algorithm designed to find the intervention moment of drive mode and active safety mode while monitoring threat vehicles ahead to overcome effects of vehicular communication on risk assessment in automated driving vehicle. Although a conventional radar system is known to be best fitted on-board ranging sensor in terms of longitudinal safety, it is generally not enough for a reliable automated driving because of sensing uncertainty of the traffic environments and incomplete perception results due to sensor limitations. This can be overcome by implementing vehicle-to-vehicle (V2V) communication which provides complementary source of target vehicle’s dynamic behavior. Using V2V communication with vehicle internal and surround information obtained from the on-board sensor system, future vehicle motion has been predicted. With accurately predicted motion of a remote vehicle, a collision risk and the automated drive mode are determined by incorporating human factor. Effects of the V2V communication on a human-centered risk assessment algorithm have been investigated through a safe triangle analysis. The computer simulation studies have been conducted in order to validate the performance of the proposed algorithm. It has been shown that the V2V communication with the proposed risk assessment algorithm allows a faster drive mode decision and active safety intervention moment. Keywords: Active safety; vehicular communication; automated vehicle; drive mod decision

1. Introduction With a significant development in sensors, actuators and other technologies related to vehicle-to-vehicle (V2V) communication technologies, numerous developments in safety systems have been achieved during the last decade. Intelligent systems based on on-board perception/detection devices have contributed to improve road safety [1]. The next step in the development of vehicle safety technology points toward vehicle-to-vehicle (V2V) communications to obtain more extensive and reliable information about vehicles in the surrounding area, representing cooperative intelligent transportation systems (C-ITS). Research projects have been conducted throughout the world to define the requirements for an appropriate vehicular communication system and its possible applications [2].

Appl. Sci. 2018, 8, 2632; doi:10.3390/app8122632

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While the in-vehicle sensors can enable a limited level of safety functionalities without the use of inter-vehicle communication, the benefits that V2X communication can bring are widely known by many automobile manufacturers, and they are the result of collaborations with the mobile industry. Toyota and Lexus have commercialized vehicles equipped with V2X communication technology from 2015 on the road in Japan. This technology provides drivers with useful and detailed surrounding vehicle and traffic signal information. In addition, they aim to deploy the technology with Dedicated Short-Range Communications (DSRC) systems on vehicles sold in the United States starting in 2021 [3]. Audi, Ford and Qualcomm announced cellular-based vehicular communication technology (C-V2X) operating across vehicles from different manufacturers. A demonstration has been performed to show the effectiveness of using C-V2X technology on the ITS spectrum in order to avoid vehicle-to-vehicle collision and improved road safety [4]. Effects of V2X communication on the automated vehicle safety have been investigated through information fusion and risk assessment. Shin et al. [5] proposed a human-centered risk assessment algorithm using V2V communication in order to determine a proper intervention timing of active safety system. A host vehicle reduces the chances of perception error and uncertainty during a sudden deceleration due to the fact that V2V communication allows a host vehicle to receive the internal sensor information of a remote vehicle and, to compute more accurate prediction. It overcomes the estimation error of a convectional radar-based advanced driver assistant system (ADAS). For fully automated driving, however, a drive mode decision function with guaranteed safety has to be integrated to threat assessment algorithm. For a safety-critical cyber-physical system of an automated electric vehicle, in [6], an artificial-neural-network (ANN)-based estimation method is announced for an accurate observation of the brake pressure of the vehicle. The ANN-based machine learning framework is used to quantitatively estimate the brake pressure of the vehicle, and it has great potential to achieve a sensorless design of the braking control system. The guaranteed safety is also crucial for a lane change decision in fully automated driving. Previous researches are analyzed a pattern of lane change of human driver data to determine lane change timing [7]. The learning-based approach for lane change using neural network to predict the maneuver trajectory [8], and a fuzzy logic for solving the modeling lane change decision making problems [9] were proposed. Since lane changing for an automated driving system requires predicting traffic by the host vehicle, a probabilistic threat assessment approach was proposed by successfully implementing a stopping sight distance [5]. To decide on the active safety control intervention moment, collision risk and human reaction time, which is inspired by stopping sight distance, are determined. Active safety systems of automated vehicle have become more intelligent incorporating human-sense, and this enhances driver acceptance. To realize drive mode decisions with high driver acceptance, the concept of decision sight distance can be considered for continuous risk assessment [10]. In this paper, the main contribution is the merging between V2V communication and a vehicle-based sensor as radar in order to get most valuable data for safety in longitudinal collision risk. The idea to incorporate such integration with a user-centered algorithm for predictive drivers’ behaviors, in the intention to include a human-like behavior in the automation, is the other contribution. Section 2 demonstrates the information fusion and fundamental descriptions for a radar, camera and the V2V communication are discussed. In Section 3, the threat assessment method describing a safe triangle analysis is demonstrated. The risk assessment algorithm with the V2V communication is investigated through simulation studies in Section 4. Finally, conclusions are provided in Section 5. 2. Sensor Characteristics and Information Fusion The characteristics for vehicular communication and conventional local sensor systems are described. The backgrounds for radar and its sensing characteristics for application to intelligent vehicle are demonstrated. For a longitudinal safety, automotive radar is known to be the best fitted vehicular surround sensing technology with respect to functionality, robustness, reliability, dependence on

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weather conditions etc. [11]. Its performance has been evaluated by comparing with the camera (vision sensor). However, due to the inherent uncertainty of the traffic environments and incomplete knowledge because of sensor limitations, V2V communication has been widely considered to provide the host vehicle with more enriched target information by fusing with the radar sensor. In this section, the advantages of radar sensor have been presented by experimentally comparing with the camera. The V2V communication information is explained as well and the comparison results of the DSRC with long-term evolution-vehicle (LTE-V) technologies are provided. In addition, a high-level data fusion architecture of radar and V2V communication is described. 2.1. On-board Ranging Sensors The radar is currently the most widely adopted sensing technology for automotive ranging applications. The radio detection and ranging (RADAR) is a system that uses electromagnetic waves to identify the range, direction, or speed of both moving and fixed objects such as aircraft, ships, motor vehicles, weather formations, and terrain. In this technology, the distance from the object is calculated through the echoes that are sent back from the object. The determination of the position of an object is done through the time-of-flight and angle measurement. In process of time-of-flight measurements, electromagnetic energy is sent toward objects and the returning echoes are observed. The measured time difference and the speed of the signal allow calculating the distance to the object. The Speed measurement is made through the Doppler effect. The base of the Doppler effect is change of wavelength due to the changing gap between waves. Although the amount of signal returned is tiny, radio signals can easily be detected and amplified. RADAR sensors are also known to have sufficient robustness to function reliably under harsh environmental conditions for extended periods of time [12]. Vision sensor systems use one or several cameras together with a microprocessor to perform image processing. Since they operate in the visible light region, their capabilities are similar to that of our own eyes. A camera-based sensor is used to detect car/obstacle in front of the subject vehicle. Camera-based longitudinal safety systems are typically used for medium range, medium field of view detection. The camera system can be charge coupled device (CCD)-based or Complementary Metal Oxide Semiconductor (CMOS)-based [13]. In their basic single camera configuration, video imagers struggle to provide ranging information. However, using the principle of triangulation coupled to obstacle recognition routines, the images viewed by camera systems may be processed to facilitate identification of obstacles and their range. Such systems provide good obstacle and environmental recognition using non-intrusive means. Cameras are able to provide information about target position for slow moving vehicles. They are capable of detecting targets even when placed at a distance from the region to be monitored. However, cameras placed in this fashion provide poor longitudinal position accuracy. An experiment was performed by using the test vehicle which is equipped with radar and vision sensor systems for measuring the range and range-rate to the proceeding vehicle under vehicle following situation in order to evaluate longitudinal safety. Data acquisition is performed at various ranges and relative velocities with constant speed of the target vehicle. In many cases, due to road curvatures, other vehicles, uneven road surfaces relative lateral position and lateral movement, the measurement range distribution might be different. Also, the exact reflection point is uncertain since the azimuth angle is not very exact, so for medium distances the main reflection point may be located on different parts of the car. Hence, test data was collected in the test road without other vehicle or obstacle. The lateral displacement of the longitudinal centerline of the subject vehicle relative to the longitudinal centerline of the target vehicle kept less than 0 m. The subject vehicle cruises behind the preceding vehicles in initial vehicle speed. The initial speed and range of subject vehicle (SV) and preceding vehicle (PV) are set to be Table 1. To validate the sensor detection performance, the GPS-based device, RT-Range, is used to measure the reference of the range and range rate data.

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The lateral displacement of the longitudinal centerline of the subject vehicle relative to the longitudinal centerline of the target vehicle kept less than 0 m. The subject vehicle cruises behind the preceding vehicles in initial vehicle speed. The initial speed and range of subject vehicle (SV) and Appl. Sci. 2018, 8, 2632 4 of 14 preceding vehicle (PV) are set to be Table 1. To validate the sensor detection performance, the GPS-based device, RT-Range, is used to measure the reference of the range and range rate data. The RT-Range device usesuses RT-3000 which is precision GPS device, and isand equipped on theon testthe vehicle The RT-Range device RT-3000 which is precision GPS device, is equipped test respectively. vehicle respectively. Table1.1.Initial Initialsetting settingcondition conditionfor forsensor sensordata dataacquisition. acquisition. Table

Host Vehicle Remote Host Vehicle RemoteVehicle Vehicle Initial Speed 30/35/40/45/50/55/60/65/70/75/80kph 20kph Initial Speed 30/35/40/45/50/55/60/65/70/75/80 kph 20 kph Initial Range 70m Initial Range 70 m In Figure 1, range error in vehicle test is depicted. As shown in Figure 1, vision sensor and radar In Figure 1, range error in vehicle test is depicted. As shown in Figure 1, vision sensor and radar sensor have similar detection error pattern within the 10 m range. On the other hand, range error in sensor have similar detection error pattern within the 10 m range. On the other hand, range error in vehicle test is depicted. As shown in Figure 1, vision sensor and radar sensor have similar detection vehicle test is depicted. As shown in Figure 1, vision sensor and radar sensor have similar detection error pattern within the 10 m range. On the other hand, range error of the vision sensor is bigger error pattern within the 10 m range. On the other hand, range error of the vision sensor is bigger than the radar sensors over the 10m range situation. In terms of range and speed measurement for than the radar sensors over the 10 m range situation. In terms of range and speed measurement for application to vehicle longitudinal safety, radar data demonstrate more accurate results compared to application to vehicle longitudinal safety, radar data demonstrate more accurate results compared camera. to camera. 15

CAM Rad

ErrorCx [m]

10

5

0

0

10

20

30 40 Clearance[m]

50

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Figure1.1.Range RangeError Errorvs. vs.Range RangeofofRadar Radarand andCamera Camera(Vision (Visionsensor). sensor). Figure

2.2. 2.2.Vehicular VehicularCommunication Communication Connected Connectedvehicle vehicletechnologies technologiesevolved evolvedfrom fromthe the1980s 1980swith withthe thewidely widelyknown knownproject projectcalled called the California Partners for Advanced Transit and Highways (PATH) project. Multi vehicles made the California Partners for Advanced Transit and Highways (PATH) project. Multi vehicles made by by Ford MotorCompany Companyare areplatooned platoonedtogether togetherwith with the the longitudinal Ford Motor longitudinal control control using using wireless wirelessLAN LAN communication well as as radar, throttle, brake actuators. In 1992, the itthe was that communicationsystems systemsasas well radar, throttle, brake actuators. In 1992, it remembered was remembered the first ever platooning experiments and demonstrations on the highway were performed successfully. that the first ever platooning experiments and demonstrations on the highway were performed Volvo demonstrated vehicular wireless communication technology for the project named Safe successfully. Road Volvo Trains demonstrated for the Environment (SARTRE) 2012. In this demonstration, Volvo FH truck drove vehicular wirelessincommunication technology fora the project named Safe around the test followed by(SARTRE) other Volvo cars with quite close inter-distance. Although the Road Trains fortrack, the Environment in 2012. In this demonstration, a Volvo FH truck drove inter-distance between the vehicles was smaller than when a human drives, it is much safer around the test track, followed by other Volvo cars with quite close inter-distance. Although the since the computer responds to even trivial or than smallwhen change is detected. the demonstration, inter-distance between the vehicles was smaller a human drives, For it is much safer since the V2V communication 802.11trivial p is used the change main communication channel. computer respondswith to even or for small is detected. For the demonstration, V2V In 2013, Hyundai Mobis and Seoul National University demonstrated communication with 802.11 p is used for the main communication channel. V2X communication enabled autonomous driving technologies in Korea, at the ITS test site in Hwaseong. Two Hyundai In 2013, Hyundai Mobis and Seoul National University demonstrated V2X communication Sonatas Mobis and one technologies Hyudai Azera are used to perform several scenarios Two withHyundai Cohda enabledfrom autonomous driving in Korea, at the ITS test site in Hwaseong. MK5 wireless communication device. The scenarios include Intersection Management Assist (IMA), Sonatas from Mobis and one Hyudai Azera are used to perform several scenarios with Cohda MK5 Electronic Emergency Brakedevice. Light and Spot Warning. wireless communication TheBlind scenarios include Intersection Management Assist (IMA), Dedicated Short Range Communications (DSRC) one of the researching hotspots, and it has already Electronic Emergency Brake Light and Blind Spot is Warning. the become V2X communication standard in some areas, such as America and the Europe. Meanwhile, the performance of DSRC in non Line-of-Sight scenarios is quite terrible and the performance declines rapidly when the number of vehicles increases to some threshold. These shortcomings of DSRC are


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Dedicated Short Range Communications (DSRC) is one of the researching hotspots, and it has already the become V2X communication standard in some areas, such as America and the Europe. Meanwhile, the performance of DSRC in non Line-of-Sight scenarios is quite terrible and the Appl. Sci. 2018, 8, 2632 5 of 14 performance declines rapidly when the number of vehicles increases to some threshold. These shortcomings of DSRC are widely admitted by researchers. Thus, LTE-V is one of the Cellular Vehicle-to-Everything (C-V2X) direct and which might take (C-V2X) place of widely admitted by researchers. Thus,communication LTE-V is one oftechnologies, the Cellular Vehicle-to-Everything DSRC.communication technologies, and which might take place of DSRC. direct Tovalidate validatethe theperformance performanceofofLTE-V, LTE-V, [14] conducted experiment with at To ShiShi et et al.al. [14] conducted an an experiment with twotwo carscars at an an intersection. During the experiment, messages sent andconclusions two conclusions are derived: intersection. During the experiment, messages werewere sent 10 Hz10Hz and two are derived: PDR PDR (Package Deliver Rate)latency, and latency, are shown in Figure 2. (Package Deliver Rate) and whichwhich are shown in Figure 2.

(a)

(b)

Figure Figure2.2. Experiment Experimentresults resultsat atintersection: intersection:(a) (a)Package PackageDeliver DeliverRate Rate(PDR); (PDR);(b) (b)Latency Latencyof ofDSRC. DSRC.

Since applications for V2V communication are susceptible to even to a bit longer latencies, Sincesafety safetyrelated related applications for V2V communication are susceptible even a bit longer this could cause fatal accidents when an emergency braking situation. To guarantee traffic safety, latencies, this could cause fatal accidents when an emergency braking situation. To guarantee traffic when controla command, braking for example, is sent to isa car, must the receive command safety,a when control command, braking for example, sentthe to car a car, thereceive car must the within 1 ms.within 1ms. command The The latency latency of of aa 4th 4th Generation Generation (4G) (4G) network, network, which which is is aa sort sort of of LTE-V, LTE-V, cannot cannot meet meet this this requirement. requirement. With Withthe thelatency latencyof ofaa4G 4Gnetwork, network,aacar cardriving drivingatat100 100km/h km/h still still moves moves 1.4 1.4 m m from fromthe the time it finds an obstacle to the time when the braking command is executed. Under the same condition, time it finds an obstacle to the time when the braking command is executed. Under the same with the latency on alatency 5G network, thenetwork, car will move 2.8move cm, and performance is comparableis condition, with the on a 5G the carjust will just this 2.8 cm, and this performance with the standard of an anti-lock system (ABS)system [15]. (ABS) [15]. comparable with the standard ofbraking an anti-lock braking A 5G-based C-V2X communication is introduced by Huawei, LG and Qualcomm. Since 5G-enabled A 5G-based C-V2X communication is introduced by Huawei, LG and Qualcomm. Since communication has ultra-lowhas latency of 1 ms withof the average communication speeds of 3.6G bit/s 5G-enabled communication ultra-low latency 1ms with the average communication speeds of using 100 MHz safety related vehicle control applications be considered. Asconsidered. the size of 3.6G bit/s usingbandwidth, 100MHz bandwidth, safety related vehicle controlcan applications can be the are getting bigger, remote computing isremote neededcomputing and the 5Gis communication is As data the size of the data areinfrastructure-based getting bigger, infrastructure-based needed and the applicable for the transmission andfor receipt of big data. Infrastructure-based computation means using 5G communication is applicable the transmission and receipt of big data. Infrastructure-based acomputation cloud or edge computing. Two computing methods have their own advantages and disadvantages. means using a cloud or edge computing. Two computing methods have their own Cloud computing has no limitation of computing has power; however,of it computing has more likely to experience advantages and disadvantages. Cloud no limitation power; however, it slow internet connection issues. Edge computing is much faster than cloud computing; however, has more likely to experience slow internet connection issues. Edge computing is much faster than the computational resource is limited or is hard to expand. cloud computing; however, the computational resource is limited or is hard to expand. Korea inin Korea, demonstrated Level 3 KoreaTelecom Telecom(KT), (KT),one oneofofthe thecellular cellularcommunication communicationproviders providers Korea, demonstrated Level autonomous driving technology with 5G5G communication. Two buses were demonstrated withwith the 5G 3 autonomous driving technology with communication. Two buses were demonstrated the connected technology by exchanging their driving information as wellas as well videoasthat shows 5G connected technology by exchanging their driving information video thatblinded shows front traffic situation. This connected technology-based service is promising for the future blinded front traffic situation. This connected technology-based service is promising for connected the future automated since it can stabilize service which service is goingwhich to be applied connected driving automated driving since it Internet-based can stabilize Internet-based is goingfortothe be safety related functions for the intelligent vehicle. Connected automated driving technology to applied for the safety related functions for the intelligent vehicle. Connected automatedneeds driving offer less than 10 ms latencies with10ms less than 1% of packet rate. 1% Thisofallows autonomous technology needs toofoffer less than of latencies with error less than packetthe error rate. This vehicle to drive safely without any issue of communication error. allows the autonomous vehicle to drive safely without any issue of communication error. In Inthe thefuture, future,the theC-V2X C-V2Xsolution solutionand andthe theconvention conventionADAS ADASlocal localsensors sensorsare areto to be be combined combinedfor for 100% 100%of ofguaranteed guaranteedsafety. safety.Even Eventhough thoughwe weuse usethe theedge edgecomputing computingwith with5G 5Gcommunication, communication,there there are arestill stillchances chancesfor forcommunication communicationerrors. errors. This This still still makes makes us us use use local local sensor-based sensor-based safety safety functions functions in terms of emergency situations such as emergency driving situations. For normal driving situations, infrastructure computing (cloud or edge) based-based autonomous driving can be performed cooperation with other surround vehicles. This allows the vehicles to drive much safer and much smoother.

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2.3. Information Fusion of Radar and V2V Communication The V2V communication, however, has several limitations when it applies to automated driving. GPS receiver has poor position estimation performance when it enters a tunnel or urban shadowing area due to low signal strength and multipath propagation error. These situations cause degradation of target tracking performance which bring about dead-reckoning mode, and consequently producing larger errors. Depending on the transmission power and receiver sensitivity, transmission messages can be received up to a distance of 1 km [16]. However, obstacles, such as buildings in urban environments, the surrounding topography in rural environments or big trucks on highways might block the wireless signal and thus make a communication between vehicles not possible. ITS-G5 (European standard for V2V communication) uses Carrier Sense Multiple Access with Collision Avoidance (CSMA-CA) to access the wireless channel and avoid the occurrence of packet collisions. However, in dense traffic scenarios, packet collisions occur and can lead to a drop in performance of a cooperative positioning approach [17]. Therefore information from V2V communication is often considered not to be reliable enough to initiate automated maneuvers. On the other hand, radar (camera and LiDAR systems as well) has its own limitations. Many heuristics are applied to the object detection, which leads to a compromise between sensitivity and false positive rates. Static objects or objects in far distances are very hard to detect reliably. Despite the shortcomings in both technologies, we found they are complimentary to each other. Radar can provide a constant passive scanning of the surrounding, catch all detection candidates and provides good relative positioning measurements. If those candidates happen to be V2V communication device equipped, the information from their DSRC messages can help to eliminate almost all 8, uncertainties and enrich those candidates with much more additional information. Appl. Sci. 2018, x FOR PEER REVIEW 7 of 16 In this way, we do not need to worry about the drops in V2V communication message reception and In low quality positioning from GPS. Figure 3., of fusion of V2V communication track and radar track is described in the master filter. In Figure 3, in the high level fusion architecture, all from sensortwo information is processed the To perform a track-to-track association for the data coming independent sources in at local sensorx radar level,and making possible applications which standard modularity andassociation simplicity. first step is to regard how have close high one track is to another so that filter, x v 2 v , the Thereforecan thebehigh level fusion is proposed andvolume it has in been successfully and wellare described in decisions made. A validation region is the state space where tracks expected. automotive applications, such as adaptive cruise control [18] and for other active safety feature [19]. This validation region is usually expressed in terms of the Mahalanobis distance. High-Level Fusion Architecture Remote Vehicle State Estimation Application-Level z host [ k ]

Ζ radar [k ]

ΖV 2V [k ]

Host Vehicle Filter

Master Filer

xˆ host [ k ] Pˆ host [ k ] Local Filter

Radar Data Process V2V Data Process

{x

radar

{x

V 2V

[ k ] Pradar [ k ]}

[ k ] PV 2V [ k ]}

V2V/Radar fusion

{xˆ [ k ] Pˆ [ k ]}

Dynamic Risk Assessment

• Collision Probability • Risk Level • Active Safety Control

Human-Active Safety Control

Figure 3. High-Level Information Fusion Architecture. Figure 3. High-Level Information Fusion Architecture.

The demonstrated high level fusion architecture for the automated driving algorithm is composed For example, Figure 4 shows an example of a radar measurement validation. The reference of two. One is a local filter and the other is master filter as seen in Figure 3. (true) position of the target-vehicle is shown in black line which is target vehicle trajectory. A For the radar data process, at a local filter, extended Kalman filters (EKF)-based interacting V2V/Radar fusion result is expressed by blue dot, which is a current estimated target-vehicle multiple model (IMM) has been implemented to compute the remote vehicles’ dynamic states location. The estimation uncertainty is shown to be a three sigma ellipse in red. The red circles are with relative position and velocity. A state augmented estimation algorithm to compensate the radar measurements, of which only the nearest to the current estimate are used to update the communication latencies is used in the V2V communication local filter. To express dynamic motion of baseline. The advantage of using a validation area based on the Mahalanobis distance instead of an the remote vehicle, we derive a kinematic vehicle model. After then, to describe the remote vehicle’s Euclidean distance is that it takes the uncertainty in each of state variables with different units, into account.

In Figure 3., fusion of V2V communication track and radar track is described in the master filter. To perform a track-to-track association for the data coming from two independent sources in local filter, x radar and x v 2 v , the first step is to regard how close one track is to another so that association decisions can be made. A validation region is the volume in state space where tracks are expected. 7 of 14 This validation region is usually expressed in terms of the Mahalanobis distance.

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High-Level Fusion Architecture CAN bus, standard measurement model is derived and the vehicle information is transmitted. The state Remote Vehicle State Estimation vector of the local filter of V2V communication is derived in (1): Application-Level z host [ k ]

Ζ radar [k ]

Host Vehicle Filter

Master Filer

xˆ host [ k ] Pˆ host [ k ] Local Filter

v2v = xrv

h

v2v Radar v2v {x θ[v2v ]}v2v k ] Pradar [ kv prv,x prv,y radar rv rv,x Data Process

v2v γrv

v2v arv,x

{xˆ [ k ] Pˆ [ k ]}

i . v2v T γDynamic Risk rv Assessment

(1)

• Collision Probability • Risk Level • Active Safety Control

Here subscript rv is remote vehicle, p is the relativeV2V/Radar position between two vehicles, θ is the relative fusion Ζ [k ] V2V {x [ k ] P [ k ]} Human-Active yaw angle between two, v is the longitudinal speed, γ is the yaw speed, a is the longitudinal speed, Data . Safety Control Process and γ is the derivative term of yaw speed. In Figure 3., fusion of V2V communication track and radar track is described in the master filter. To perform a track-to-track association for the data coming from two independent sources in local filter, xradar and xv2v , the first stepFigure is to regard how close one track is toArchitecture. another so that association decisions 3. High-Level Information Fusion can be made. A validation region is the volume in state space where tracks are expected. This validation regionFor is usually expressed terms of Mahalanobis distance. example, Figure 4inshows anthe example of a radar measurement validation. The reference Forposition example, 4 shows an is example radarline measurement validation. referenceA (true) of Figure the target-vehicle shown of in ablack which is target vehicleThe trajectory. (true) position of the target-vehicle is shown in black line which is target vehicle trajectory. V2V/Radar fusion result is expressed by blue dot, which is a current estimated target-vehicle Alocation. V2V/Radar resultuncertainty is expressedisbyshown blue dot, which is a current estimated target-vehicle location. The fusion estimation to be a three sigma ellipse in red. The red circles are The estimation uncertainty is shown a threeto sigma ellipse in red. The circles are radar radar measurements, of which only to thebenearest the current estimate arered used to update the measurements, of which of only theanearest to the current are used todistance update the baseline. baseline. The advantage using validation area based estimate on the Mahalanobis instead of an The advantage of using a validation area based on the Mahalanobis distancewith instead of an Euclidean Euclidean distance is that it takes the uncertainty in each of state variables different units, into distance account.is that it takes the uncertainty in each of state variables with different units, into account. V 2V

V 2V

V 2V

Figure Radarmeasurements measurements (red) need be associated with the current Figure 4. 4. Radar (red) need to betoassociated with the current relativerelative positionposition estimate estimate (blue). The Mahalanobis takesthe also the current estimation uncertainty ellipse) (blue). The Mahalanobis distancedistance takes also current estimation uncertainty (blue(blue ellipse) into into account. account.

Given Given two two tracks, tracks, radar radar and and V2V V2V with with mean mean estimates estimates and and covariances covariances represented represented by by (x( xradar k , P k and ( x k , P k , the Mahalanobis distance is defined as: [ ] [ ]) [ ] [ ]) [ ] radar [ ] xv 2 v [ k ] ,v2v Pv 2 v [ k ]) , the Mahalanobis distance is defined as: v2v radar k , P radar k ) and ( q −1 (2) χradar,v2v = (xradar [k] − xv2v [k])T · ( Pradar [k] + Pv2v [k]) · (xradar [k ] − xv2v [k ]) T -1 (2) χ radar ,v 2 v = ( xradar [ k ] − xv 2 v [ k ]) ⋅ ( Pradar [ k ] + Pv 2v [ k ]) ⋅ ( xradar [ k ] − xv 2v [ k ]) Both the uncertainty of the state estimate and that of the measurement influence the Mahalanobis distance. The smaller these uncertainties are, the higher the associated distance. For the experimental the Mahalanobis threshold wasofempirically set to 5, ininfluence order to get Both thesections, uncertainty of the statedistance estimate and that the measurement the aMahalanobis good rejection of outlier measurements, due toare, other andassociated scatter the state estimate distance. Theradar smaller these uncertainties thevehicles higher the distance. For the and that of thesections, measurement influence thedistance Mahalanobis distance. The smallerset these are,a experimental the Mahalanobis threshold was empirically to 5,uncertainties in order to get the higher the associated distance. For the experimental sections, the Mahalanobis distance threshold was empirically set to 5, in order to get a good rejection of outlier radar measurements, due to other vehicles and scatter. Data association techniques are used for relating measurements to targets [20]. Unlike the Nearest-Neighbor approach, where only the measurement which has the

good rejection of outlier radar measurements, due to other vehicles and scatter the state estimate and that of the measurement influence the Mahalanobis distance. The smaller these uncertainties are, the higher the associated distance. For the experimental sections, the Mahalanobis distance threshold was empirically set to 5, in order to get a good rejection of outlier radar measurements, due to other Appl. Sci. 2018, 8, 2632 8 of 14 vehicles and scatter. Data association techniques are used for relating measurements to targets [20]. Unlike the Nearest-Neighbor approach, where only the measurement which has the smallest Mahalanobis distancedistance to the to target is used to update thethe measurement; smallest Mahalanobis the target is used to update measurement;ininprobabilistic probabilistic data association all measurements in the validation region are are taken taken into into account. account. 3. Automated Driving Risk Assessment It is well known that a human-centered risk assessment algorithm is beneficial to avoid collision at any speed range with aa proper For a fully proper intervention intervention timing timing of of active active safety safety function function [5]. [5]. For automated drive, drive, drive drive mode mode decision decision function, function, aa lane lane change change or or a lane keeping, is to be activated automated while ensuring safety. A decision sight distance is implemented to facilitate adequate mimicking of mirror human internal states by computing human reaction time for safety human senses sensesand andallows allowstoto mirror human internal states by computing human reaction time for and smoother operations. In this In paper, algorithm is extended to activate drive mode decision safety and smoother operations. this the paper, the algorithm is extended to a activate a drive mode function function incorporating human reaction based on based decision distance. 5 shows decision incorporating human time reaction time onsight decision sight Figure distance. Figurean 5 overview the human-centered risk assessmentrisk algorithm including two control functions: active shows anonoverview on the human-centered assessment algorithm including twoancontrol safety mode, and a lane change mode decision). Themode proposed safe triangle analysis is functions: an active safety mode,mode and a(drive lane change mode (drive decision). The proposed safe based on the peak (maximum) collision probability, maxCp, and the human reaction time with respect triangle analysis is based on the peak (maximum) collision probability, maxCp, and the human to most probable distance, DpmaxCp .collision distance, DpmaxCp. reaction time withpredicted respect tocollision most probable predicted

Figure 5. Human Human Centered Centered Risk Risk Assessment-based Assessment-based Safe Triangle Analysis. Figure

A safe risk and to to decide mode intervention timing for A safe triangle triangle analysis analysisisisproposed proposedtotoassess assessthe the risk and decide mode intervention timing a fully automated drive. It allows the system to decide the drive mode by avoiding the collision at for a fully automated drive. It allows the system to decide the drive mode by avoiding the collision any driving situation. For a longitudinal safety, the safe triangle analysis detects drive mode decision at any driving situation. For a longitudinal safety, the safe triangle analysis detects drive mode timing before active safety intervention function function is activated. With theWith V2V the communication, the area decision timing before active safety intervention is activated. V2V communication, of safe triangle is enlarged due to accurate information of front vehicle dynamic states. the area of safe triangle is enlarged due to accurate information of front vehicle dynamic states. 3.1. Risk Assessment for Drive Mode Decision 3.1. Risk Assessment for Drive Mode Decision A decision sight distance (DSD) [10] is used to extend the human-centered risk assessment algorithm and it allows to determine the intervention timing of a drive mode (lane change) by considering safety and smoother operation of vehicle. The experimentally acquired and fitted data for the decision sight distance with a range of speed is described in Table 2.

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Table 2. Decision sight distance for a range of speed. Speed (km/h)

Decision Sight Distance for Mode Decision (m)

50 60 70 80 90 100 110 120 130

170 205 235 270 315 355 380 415 450

The DSD is the distance required for a driver to handle the dangerous situation, select a proper velocity and path, and start and complete the required maneuver safely. A 14 s of human driver reaction time for decision sight situations incorporates the abilities of most drivers, including those of older drivers. This sight distance is for the recommended urban driving condition. As seen in Table 2, a DSD formula is described incorporating driver perception-reaction time with deceleration distance to derive the design speed. DSD (t)

= Driver perception − reaction distance + braking distance = 0.278 · v x (t) · Tr + 0.039 ·

v x ( t )2 a

(3)

where, DSD = required decision sight distance [m] v x = longitudinal Speed [m/s] Tr = brake reaction time = 14 [s] a = deceleration rate = 3.4 [m/s2 ] 3.2. Safe Triangle Analysis It is widely known that the best trade-off between human reaction time and collision risk computed from the machine is proper intervention timing for active safety function. To maximize driver acceptance and vehicle safety, these two factors are considered for whole driving speed range. For a human factor, driver’s perception reaction time had been implemented and a stopping sight distance formula is introduced. The collision risk had been computed real time by finding corresponding human reaction time. This means that embedded human factor is interacting with machine which numerically computes the risky situation with Monte-Carlo simulation process. In addition, this graph is called single safety plane. Here a safe triangle is introduced in order to consider both normal and emergency driving situations. The driving situation changes many times and situation awareness is getting more important. The safe triangle includes two intervention points so that two modes are activated with proper timing. The algorithm aims to decide a proper intervention timing of two control functions for a fully automated driving. For a longitudinal safety, the safe triangle analysis detects a drive mode decision timing before active safety intervention function is activated. It can be achieved by finding the intervention point (moment) of active safety and drive mode. Between maximum collision risk and corresponding predicted human reaction time with respect to DpmaxCp , each crossing point is a best intervention point. To improve driver acceptance while ensuring safety and driving efficiency, a human driver’s ability has to be implemented to active safety control and drive mode decision. It is well known that human reaction time and probabilistic collision risk well express a best trade-off between a human sense and automated function [5] . In Figure 5, a remote vehicle sudden braking of 0.2 g during which a host vehicle is initially following the remote vehicle by driving 110 km/h of constant speed. Here two maximum predicted


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well known that human reaction time and probabilistic collision risk well express a best trade-off between a human sense and automated function [5] . In Figure 5, a remote vehicle sudden braking of 0.2g during which a host vehicle is initially Appl. Sci. 2018, 8, 2632 10 of 14 following the remote vehicle by driving 110 km/h of constant speed. Here two maximum predicted human reaction times of two control functions are set to be 2.5 and 14 seconds, respectively. Since previous experimental theare validity of 2.5 theand 2.5 14 and 14 seconds as theprevious optimal human reaction times ofdata two [9,21] controlverified functions set to be s, respectively. Since design perception-reaction time safety anddesign smoother operations of experimental data [9,21] verified the(human validity reaction of the 2.5 time) and 14for s asa the optimal perception-reaction human-like probability, 0 to operations 100%, andofpredicted reaction 0 to 2.5 time (humandriving. reaction The time)collision for a safety and smoother human-like driving.time, The collision seconds, are0corresponding each other as described 5. More than 14 seconds human probability, to 100%, and to predicted reaction time, 0 in to Figure 2.5 s, are corresponding to eachofother as reaction describes enough a ssituation forreaction a drivertime to determine mode as a for lanea describedtime in Figure 5. More thanof14 of human describes drive enough of a such situation keeping/changing than that demonstrates collision probability be driver to determinesafely. drive However, mode suchsmaller as a lane keeping/changing safely. However, smallershould than that considered due to the fact that the driver the safety system to be cautious andand to predict the demonstrates collision probability shouldand be considered due tohave the fact that the driver the safety remote system vehicle’s have to bebehavior. cautious and to predict the remote vehicle’s behavior. 4. Simulation Studies: Effects Effects of V2V Communication We have used Matlab/Simulink 2017a which which is is introduced introduced in Natick, MA, United States - (9 Mar Matlab/Simulink 2017a connected to to Carsim8.02 Carsim8.02 software software which which is is introduced introduced in in Ann Ann Arbor, Arbor, MI, MI, 2017) by Mathworks, connected United States (2010) by Mechanical Simulation Corporation, for our simulations in order to validate the effectiveness of the V2V communication communication on on the the automated automated vehicles. vehicles. The Thehuman human–centered –centered risk assessment has been used for the simulation It considers considers the the intervention intervention of two control assessment simulation studies. It functions and its perception performance is investigated on the safe triangle functions triangle analysis. A predicted collision risk with two predicted human reaction time, and each corresponding intervention timing of two control functions are the the main main performance performance metrics. metrics. To To show show the the over/under over/under estimate of the conventional radar only system by comparing with the V2V communication, the predicted risk with implemented. TheThe active safety intervention moment showsshows a delayaofdelay the decision the reaction reactiontime timeis is implemented. active safety intervention moment of the making process. decision making process. Two simulation studies are investigated in this paper. The first is the remote vehicle’s sudden asas seen inin Figure 6a.6a. In In addition, Figure 6b braking right right after afterthe theparked parkedremote remotevehicle’s vehicle’ssudden suddencut cutinin seen Figure addition, Figure shows thethe scenario for for thethe response of the hosthost vehicle when the the preceding remote vehicle applies the 6b shows scenario response of the vehicle when preceding remote vehicle applies hardhard brake on the the brake on highway. the highway.

(a)

(b)

Figure 6. Simulation Simulation Scenarios: Scenarios: (a) (a) Parked Parked Remote Remote Vehicle Vehicle Cut-in Cut-in and and Braking; Braking; (b) (b) Remote Remote Vehicle Braking on the Highway. Highway.

Figure6a, 6a,the the host vehicle is cruising at a constant of 30and km/h and the remote In Figure host vehicle is cruising at a constant speed speed of 30 km/h the remote vehicle is vehicle is suddenly braking right after the parked remote vehicle cutting in with the range of 15 m. suddenly braking right after the parked remote vehicle cutting in with the range of 15 meters. As As demonstrated in Figure remote vehicle applies hard brake of 0.35 g. demonstrated in Figure 6a, 6a, thethe remote vehicle applies hard brake of 0.35g. 4.1. Remote Remote Vehicle Vehicle Dynamic Dynamic State State Estimation Estimation Performance 4.1. Performance Figure 7 illustrates the remote vehicle state estimation results for the remote vehicle braking scenario on the straight road. The state estimation results in Figure 7 shows that V2V/Radar fusion algorithm outperforms during sudden deceleration of the remote vehicle from 6 s. Since remote


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Longitudinal Velocity Longitudinal Velocity [kph] [kph]

2

2 Longitudinal Acceleration Longitudinal Acceleration [m/s ] [m/s ]

Figure 7 illustrates the remote vehicle state estimation results for the remote vehicle braking scenario on the straight road. The state estimation results in Figure 7 shows that V2V/Radar fusion Figure 8,7 2632 illustrates the remote vehicle state estimation results for the remote vehicle braking Appl. Sci. 2018, 11 of 14 algorithm outperforms during sudden deceleration of the remote vehicle from 6s. Since remote scenario on the straight road. The state estimation results in Figure 7 shows that V2V/Radar fusion vehicle’s accurate vehicle internal sensor signals such as longitudinal velocity and longitudinal algorithm outperforms during sudden deceleration of the remote vehicle from 6s. Since remote acceleration are sent to theinternal host vehicle, the host vehicle does not needvelocity to estimate the dynamic vehicle’s vehicle’s accurate accurate vehicle vehicle internal sensor sensor signals signals such such as as longitudinal longitudinal velocity and and longitudinal longitudinal behavior of the remote vehicle with certain level of uncertainty and error. Thus, target vehicle acceleration acceleration are are sent sent to to the the host host vehicle, vehicle, the the host host vehicle vehicle does does not not need need to to estimate estimate the the dynamic dynamic tracking performance is significantly improvedlevel because of V2V communication compared tovehicle radar behavior behavior of of the the remote remote vehicle vehicle with with certain certain levelofofuncertainty uncertaintyand anderror. error. Thus, Thus, target target vehicle only situation. tracking tracking performance performance is is significantly significantly improved improved because because of of V2V V2V communication communication compared compared to to radar radar only situation. only situation. 6 85 80 85 75 80 70 75 Sent Message Radar Only V2V/Radar Fusion Sent Message

60 65

55

2 0

-2

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55 60

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Radar Only V2V/Radar Fusion

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(a) Information Fusion time Results [sec] of Longitudinal Velocity

4

-4 -2

2Radar3Only 4 5 V2V/Radar time Fusion [sec]

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Sent Message Radar Only V2V/Radar Fusion Sent Message

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(a) Information Fusion Results of Longitudinal

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time [sec] 1

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Results (b) Information Fusiontime [sec] of Longitudinal Acceleration

(b) Information Fusion Results of Longitudinal

Velocity Acceleration Figure 7. Remote vehicle state estimation results: (a) Longitudinal Velocity; (b) Longitudinal Figure 7. Remote vehicle state estimation results: (a) Longitudinal Velocity; (b) Longitudinal Acceleration. Acceleration. Figure 7. Remote vehicle state estimation results: (a) Longitudinal Velocity; (b) Longitudinal 4.2. Active Safety Control Acceleration.

4.2. Active Safety Control Due to the fact that the V2V communication allows the host vehicle to use more enriched 4.2. Active Control Due toSafety thethe fact that vehicle’s the V2V state communication the host vehicle fusion to usealgorithm more enriched information of remote compared toallows radar only, V2V/Radar shows information of the remote vehicle’s state compared to radar only, V2V/Radar fusion algorithm more Due reliable and fact accurate detection performance of thethe remote it applies sudden to the that target the V2V communication allows host vehicle vehiclewhen to use more enriched shows more and8b. accurate target detection performance of the remote vehicle when it applies brake as seenreliable in Figure The simulation demonstrate active safety control intervention information of the remote vehicle’s stateresults compared to radarthe only, V2V/Radar fusion algorithm sudden brake asdetected seen in earlier Figureby 8b.0.5The simulation demonstrate theThe active safety control moment can be s because theresults V2V communication. radar only in shows more reliable and accurate target detectionofperformance of the remote vehicle when it case applies intervention moment can be detected earlier by 0.5 seconds because of the V2V communication. The Figure has underestimate issue when it compares to results V2V/Radar fusion. the active safety control sudden8abrake as seen in Figure 8b. The simulation demonstrate radar only case in Figure 8a has underestimate issue when it compares to V2V/Radar fusion. intervention moment can be detected earlier by 0.5 seconds because of the V2V communication. The radar only case in Figure 8a has underestimate issue when it compares to V2V/Radar fusion.

(a)

(b)

(a) (b) Figure 8. Simulation Simulationresults resultsforfor braking scenario in Figure (a) only; Radar only; (b)V2V/Radar Figure 8. thethe braking scenario in Figure 6a: (a)6a: Radar (b) V2V/Radar fusion. fusion. Figure 8. Simulation 4.3. Drive Mode Decision results for the braking scenario in Figure 6a: (a) Radar only; (b)V2V/Radar fusion.

4.3. Drive Mode Decision The human centered risk assessment algorithm works with a probabilistic threat assessment incorporating human factor,risk andassessment decision of two control works functions’ intervention moment. The algorithm 4.3. Drive Mode Decision The human centered algorithm with a probabilistic threat assessment computes maxCp (peak collision risk) and Dp (most probable predicted collision distance) maxCp incorporating human factor, and decision of two control functions’ intervention moment. from The The human centered risk assessment algorithm works with a probabilistic threat assessment the preceding threat vehicles. algorithm computes maxCp (peak collision risk) and DpmaxCp (most probable predicted collision incorporating human factor, and decision of two control functions’ intervention moment. The Figure 9 demonstrates results when the preceding remote vehicle applies the brake distance) from the precedingsimulation threat vehicles. algorithm computes maxCp (peak collision risk) and DpmaxCp (most probable predicted collision on the highway as described in Figure 6b. The host vehicle speed is cruising with the longitudinal distance) from the preceding threat vehicles. velocity of 100 km/h for whole period of simulation. The remote vehicle applies the brake at 4 s for two cases: V2V/Radar fusion and radar only cases. As seen in Figure 9c, the radar only case shows


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Figure 9 demonstrates simulation results when the preceding remote vehicle applies the brake on the highway as described in Figure 6 b. The host vehicle speed is cruising with the longitudinal Appl. Sci. 2018, 8, 2632 12 of 14 velocity of 100 km/h for whole period of simulation. The remote vehicle applies the brake at 4 seconds for two cases: V2V/Radar fusion and radar only cases. As seen in Figure 9c, the radar only case shows the decision moment of drive at 9.2 s. The V2V/Radar however, shows the decision moment of drive mode at 9.2mode s. The V2V/Radar fusionfusion case, case, however, shows earlier earlier timingmode for drive mode decision 1.1to seconds duethat to the fact that V2V/Radar fusion algorithm timing for drive decision of 1.1 s of due the fact V2V/Radar fusion algorithm has lower has lower prediction uncertainty levelonly thancase. radarThis only means case. This means that the V2V communication prediction uncertainty level than radar that the V2V communication has much has much faster drive mode decision timing by reducing the chances of underestimate of radar only faster drive mode decision timing by reducing the chances of underestimate of radar only case as seen case as seen in Figure 9a,b. in Figure 9a,b.

(a)

(b)

(c) 9. Parameters schematic view collisionprobability-based probability-based risk (a) (a) safesafe triangle FigureFigure 9. Parameters schematic view ofof collision riskassessment: assessment: triangle analysis: V2V/Radar fusion; (b) safe triangle analysis: Radar only; (c) Peak collision risk of vehicles: the analysis: V2V/Radar fusion; (b) safe triangle analysis: Radar only; (c) Peak collision risk of the vehicles: V2V/Radar fusion and Radar only. V2V/Radar fusion and Radar only.

Figure 9 also describe the simulation results for the intervention moment of the active safety

Figure 9 also describe the simulation results for the intervention moment of the active function. In the case of V2V/Radar fusion, the collision risk of sudden deceleration of the remote safety function. In the case of V2V/Radar fusion, the collision risk of sudden deceleration of the vehicle can be more precisely and reliably computed on the host vehicle as seen in Figure 9c. The remote vehicle can be more precisely and reliably computed on the vehicle as seen in Figure active safety intervention moment of V2V/Radar fusion described in host Figure 9a is much earlier than 9c. The active safety intervention moment of V2V/Radar fusion described in Figure 9a is much that of radar only case in Figure 9b by 0.6 seconds. For an emergency situation, even 0.1 seconds earlier is than that of for radar onlycrash. case Thus, in Figure 9b by 0.6 s. For an emergency situation, even s is crucial crucial vehicle 0.6 seconds is quite a long amount of time to prevent the0.1 collision and for vehicle crash. Thus, s is quite of time to prevent the collision and give driver more give driver more0.6 reaction timeatolong stopamount the vehicle. Figure 9a and b show the area of safe triangle of V2V/Radar fusion is more enlarged by 13.65 % reaction time to stop the vehicle. than that of radar only case. This is due to the fact that the fusion distanceisbetween two crossing pointsthan Figure 9a,b show the area of safe triangle of V2V/Radar more enlarged by 13.65% that of radar only case. This is due to the fact that the distance between two crossing points between the active safety control and the drive mode decision is elongated in the case of V2V/Radar fusion. In other words, V2V communication has much enough time to react hazardous situation by reducing the chances of underestimate risky situation. The safe triangle analysis will be used in the vehicle and it is expected to compute the size of the triangle real time.

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5. Conclusions This paper describes a human-centered risk assessment algorithm with two control functions for automated driving vehicles. The proposed algorithm shows effectiveness of vehicle-to-vehicle communication which significantly improves the safety of automated driving vehicle with faster intervention moment of active safety control and drive mode decision. Since a 5G network-based vehicle-to-vehicle communication overcomes two issues of conventional 4G network-based system; a latency and a package deliver rate (PDR). This provides a host vehicle with more accurate remote vehicle information, and allows predicting the motion of the remote vehicle more accurately. To decide proper intervention moment of active safety control and drive mode, a collision risk and two human reaction times are computed. By incorporating two human factors, the risk assessment system of automated vehicle has been extended to fully automated driving, incorporating human factors. The simulation results show that the proposed algorithm with the V2V communication allows a faster drive mode decision and active safety control for application to automated driving. The effects of V2V communication have been investigated through safe triangle analysis and this shows enlarged area of triangle by 13.65%. This results in proper intervention timing of two control functions with more enough time to react hazardous situation. The proper intervention timing improves driver acceptance since the human-centered algorithm considers human reaction time which reflects the capability of most of the human driver while making drivers understand the situation of the autonomous vehicle better than conventional ADAS. This paper considers full speed range of risk assessment method and drive mode decision with the active safety function is also considered. These two functions are computed with two kinds of reaction time from stopping sight distance and decision sight distance. In the future, coordination between edge computing and in-vehicle local computing will be implemented and this will be our future research topic. By using 5G-based C-V2X and edge computing, the computation resource is more enriched. In addition, it is expected that real time implementation of particle filter on the test vehicle is possible with the guaranteed performance. For normal driving mode, edge computing-based automated driving is used. When the vehicle encounters emergency situation, the vehicle is controlled by the local computer using vehicle local sensors such as radar and LiDAR and vision. At the same time, vehicle tests will be performed to validate communication performance and the vehicle safety control performance. Communication and sensor information fusion performance is another research topic in the near future Author Contributions: Conceptualization, D.S. and K.P.; Methodology, D.S.; Software, D.S.; Validation, D.S., K.P. and M.P.; Formal Analysis, K.P.; Investigation, D.S.; Resources, K.P.; Data Curation, D.S.; Writing—Original Draft Preparation, D.S.; Writing—Review and Editing, M.P.; Visualization, D.S.; Supervision, M.P.; Project Administration, D.S.; Funding Acquisition, M.P. Acknowledgments: This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2017-2013-0-00680) supervised by the IITP(Institute for Information &communications Technology Promotion). It was also supported by the 2018 National Research Projects of Naval Research Center, Republic of Korea Naval Academy. Conflicts of Interest: The authors declare no conflicts of interest.

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