Data Collection & Processing in Vehicle-Area

Data Collection & Processing in Vehicle-Area-Networks: A Review Miad Faezipour, Adnan Saeed, Rasoul Yousefi, Sarah Ostadabbas, Mehrdad Nourani Center for Integrated Circuits & Systems The University of Texas at Dallas, Richardson, TX 75083 {mxf042000, axs055200, rxy091020, sarahostad, nourani}@utdallas.edu

Sateesh Addepalli Cisco Systems Inc. San Jose, CA 95134 [email protected]

receive data. Various interactions among participating elements may include lane-keeping signals, obstacle detection, adaptive cruise control, navigation data, driver status, etc. This provides automatic driver assistance that not only improves driver safety, but also creates a cooperative environment where the right information is provided at the right time. For example, drivers/vehicles can optionally exchange useful information such as weather conditions, traffic jam, or business/pleasure information such as shopping/dining deals as they travel along the same road. The ultimate goal is to provide an accident-free environment and to fully implement the zero-accident-car by the help of vehicle area networks [2]. This paper surveys the main innovations on vehiculararea-networks (VAN) focusing on the recent developments of intelligent transportation systems (ITS) for intra-vehicle and inter vehicle-area-networks to assist driver safety. We provide a brief insight to the main communication and networking components of vehicle-area-networks. We discuss and envision an efficient platform with image-based tools and a car/driver behavioral analysis capability to enhance active-safety features in future VANs.

Abstract— By 2016, inter/intra-vehicle connectivity is expected to become as important as safety and fuel-efficiency factors of any vehicle. This paper surveys the innovations and ongoing researches in vehicle-area-networks (VAN) which form the backbone of future intelligent transportation systems. Progress and challenges toward effective VAN are reviewed. In particular, an in-vehicle platform using image-based tools and behavior analysis capability is discussed that aims at enhancing safety features of future VANs. Index Terms— In-vehicle area networks, behavior analysis, image-processing, sensor.

I. I NTRODUCTION Recently, the smart vehicle research has received much attention to assist drivers and ultimately revolutionize the way vehicles, road sensors and drivers communicate in future. The key objective is to improve driver and vehicle safety. The National Transportation Safety Board (NTSB) reports that U.S. highways on average experience 43,300 fatalities per year. Everyday, more than 16,000 crashes occur on American highways, mainly due to driver error, poor judgment, drowsiness or distraction [1]. The US National Highway Traffic Safety Administration (NHTSA) estimates that in USA alone, each year approximately 100,000 crashes (about 2% of all) are caused primarily by driver drowsiness or fatigue [1]. Thus, incorporating automatic active vehicle safety techniques, such as driver fatigue detection, warning mechanisms based on surrounding events/objects and other driver assist tools into vehicles may significantly help in preventing accidents and increase crash survivability. Based on the state of knowledge in vehicle-area-network (VAN), we differentiate two types of communication requirements/networks: • Intra (In-Vehicle) VAN: Intra-vehicle networks deal with the data communication network of on-board-equipment (OBE) for assessing a driver’s behavior or a vehicle’s performance. Nowadays, passive and active safety techniques are being employed and devised in vehicles [2]. Passive vehicle safety includes a set of tools such as seat-belts, air bags, etc., that improve safety in the event of an accident. Active vehicle safety techniques consist of a variety of techniques such as on-board driver assistance tools (e.g. driver fatigue detection), lane-keeping or congestion control tools, etc., that altogether, proactively try to prevent car accidents. • Inter VAN: Inter-vehicle communication is also a key element of VAN which includes (i) vehicle-to-vehicle (V2V), (ii) vehicle-to-broadband cloud communication (V2C) and (iii) vehicle-to-roadside-infrastructure communication (V2I) using road-side-units (RSU) [3] [4] [5]. An intelligent VAN is a network of vehicles that interact with one another and with infrastructure to transmit and

II. I NTRA (I N -V EHICLE ) VAN Intelligent intra vehicle communication systems for detecting a vehicle’s performance/operation and especially a driver’s fatigue and drowsiness, is critical for driver and public safety. This is becoming a major stream of research in the area of intelligent vehicle systems. Intelligent in-vehicle systems, mainly, on-board equipment (OBE) collect information from the driver/vehicle and analyze/classify the data collectively to predict or detect driver fatigue. Machine learning techniques are extensively used for such data classification [6]. This platform collects standard vehicle information such as the speed, pressure amount and usage pattern on the brake/gas pedal, steering wheel rotation, global positioning system (GPS) routing, etc. [7]. In addition to standard vehicle information, driver behavioral information such as facial expression (e.g. blink-rate, yawning, eyebrow raise, chin-drop, head movements) can be collected [3]. Even physiological signals such as heart-rate variability can be collected to determine drowsiness/non-alert level of the driver [8]. Researchers have even reported that there is a high correlation between the level of alertness and the power signal in the alpha and theta band of the EEG signal [9]. Other physiological signals such as heart rate (e.g. using seat-installed sensors), electrooculogram (EOG) using a camera, blood pressure (BP), and sweating on the palm (e.g. when driver touches the steering wheel) could be used for fatigue detection and sleep episode prediction. In such platform, 1

sensors and audio/video (i.e. microphone/camera) can be used for collecting signals for this purpose. This platform itself is an in-vehicle network that engages potentially a large number of sensors in the car to collect vehicle/driver information. It may optionally transmit these data to a monitoring data center for processing and receive feedbacks, e.g. a warning/alert signal, for the driver. Top Challenges in intra-vehicle VAN include: • Car-Suited Physiological Sensors: The success of driver behavioral analysis depends on accurate and robust data collection. While some preliminary works were reported in the literature [3] [10], implementing accurate sensors (e.g. heart rate, EOG, BP, Sweat) and proper mounting (e.g. to be none or minimally visible), require more attention. • In-Vehicle Data Analysis: Certain data processing may be needed in vehicle because of urgency and/or due to lack of connection to the base. This challenge deals with identifying such data (e.g. fatigue, · · ·) and processing them using a mix of digital signal processing and machine learning techniques that can run on an embedded processor/microcontroller. • Vehicle Controller Area Network: The vehicle controller area network (CAN) is a serial bus communications protocol that allows access to the vehicle internal system through an embedded networked control system [11]. Wireless sensors planted in the car (e.g. temperature, tire-road friction, stability control, brake force) and reporting data to the central system are the challenges of integrating CAN [12] for VAN purposes. • Generic Plug & Play Gateway: As the interest for in-vehicle connectivity is growing, a single preferably wireless platform is needed to connect all the in-vehicle and mobile devices.

In this platform, cellular/Wi-fi devices can be used for both short and long range inter-vehicle communication. Wireless connections, that meet the power/bandwidth requirements, such as GPRS used in 3G cellular communication systems, 4G ultra high speed mobile broadband such as long term evolution (LTE) mobile broadband [19], and mobile Wi-fi hotspot provide standard connections for multiple vehicles and their devices. Each application requires different sensors, processing units and even perhaps actuators. There are already some products in the market, e.g. lane-passing alarm in class W163-M Mercedes Benz, brake assist/navigation link in Toyota, tire sensors in Fiat, blind-spot-detection in BMW, collision mitigation brakes in Honda, etc. However, this field is far from maturity. In fact, the sky is the limit for innovative systems and gadgets that fit into consumer’s budget and need. Cooperative or cognitive communications among vehicles are at present in infancy phase. The goal is to facilitate data exchange and create a highly informative network. Though the LTE connected car initiative (www.ngconnect.org), and the iDrive system with Internet connectivity (www.bmwblog.com) have been recently introduced, development of a heterogeneous network architecture which enables wire-speed, robust, seamless and secure communication is an on-going effort. There are many open questions that demand answers including: What one vehicle can or cannot broadcast/receive, how to format the packets for more effective distribution. Such distribution is in particular challenging due to the time limit (order of several seconds at most) that vehicles are within access range of each other.

III. I NTER -V EHICLE VAN

B. Vehicle-to-Cloud (V2C) Communication

A. Vehicle-to-Vehicle (V2V) Communication

Vehicles communicating with a broadband cloud, e.g. a monitoring data center in a vehicle-area-network, opens new door for many useful applications. Vehicles may communicate via wireless broadband mechanisms such as 3G/4G (HSI). Going forward, high-speed 4G mobile broadband technologies such as LTE, 802.16m based WiMAX achieving speed in excess of 100Mbps will be of high interest. This type of communication will specifically be useful for active driver assistance and vehicle/driver tracking in network fleet management. In particular, the cell-phone device could be itself used as a gateway in this platform to send/receive data to/from a central monitoring data centers connected to the broadband cloud [5]. V2C networks can provide useful information in two ways: 1) outgoing data that may include: (a) vehicle-centric information (e.g. speed, global positioning/routing, device functionality and performance); and (b) driver-centric information such as driver’s specific behavior (e.g. drowsiness, length of continuous driving), audio/video, etc. All of these data may be optionally forwarded to a central monitoring server/system for further analysis and storage. 2) in-coming data that may include receiving data from a central office for various communications with driver and/or vehicle system. Additional reasons to connect to the cloud may include: 1) Infotainment, Entertainment (e.g. multimedia streaming), 2)

V2V communication can provide data exchange platform, expand driver assistance, and facilitate active safety vehicle system development. Driver assistance is provided using cooperative communication among vehicles to adaptively broadcast and/or share information or warning messages for the driver. This can be further customized for specific group of people in the community such as elderly drivers. Lane keeping [13], steering control and parking assistance [14] [4], obstacle detection, inter-vehicle spacing, and driver/vehicles exchanging optional/useful information along the same road [3] [15], fall in V2V communication category. Wireless connectivity, including wireless LAN localization can be dedicated for this type of vehicle-area-network communication. When driving, vehicles can observe various wireless signals such as GSM, cell tower signal, FM-AM radio, radar signals, GPS and wireless LAN signals. In particular, differential GPS has become prominent to determine more accurate coordinates for localization [16]. In wireless LAN, many access points emit beacons periodically. If a vehicle enters a wireless LAN-available area, the vehicle can get beacons’ information such as service-set identifier (SSID), MAC address (BSSID), signal strength and can estimate its position in relation to the access point [17]. In addition, vehicle’s speed can be estimated by comparing the difference in signal strength distribution among other mobilities [18]. 2

Internet, 3) Automotive as well as location based services, 4) Connecting to the car dealers and auto service centers, etc. Top challenges include: 1) There are questions such as what information to collect, what to filter, what to process in-vehicle, and what to send/receive to or from the data center, etc. All these account for the V2B communication latency which should be addressed to improve the efficiency and preserve the real-time nature of the overall network. 2) Design of a preferably uniform intelligent gateway using Wifi, cellular and other broadband networks for plug & play devices and to setup the network remains an open issue in V2C communication. 3) There are suggestions for leaving all or most of the data processing to be performed to the data center, as it may have almost unlimited computational power. Challenges lie in devising such processing data centers dedicated for this purpose. In addition, distributed vs. centralized data processing, and in-vehicle vs. in-data-center processing remain as open research areas. 4) Fleet management/monitoring are challenging applications of V2B communication in which the cloud architecture should keep track of the activities of each vehicle within its network.

fines the V2V and V2I communication protocols for highspeed vehicles and mainly addresses design challenges at the physical (PHY) level. In U.S., the Dedicated Short Range Communications (DSRC) of 5.9 GHz, i.e. the licensed ITS band of 5.85-5.925 GHz, is used for this purpose. • IEEE 1609: A higher layer standard, which IEEE 802.11p is based upon, is the IEEE 1609 standard [22] providing ubiquitous vehicular communication among different automobile vendors and manufactures. IEEE 1609 includes a family of standards for WAVE. It defines the architecture, organization/management structure, communication model, security mechanisms and physical access. These features, collectively, facilitate secure V2V and V2I wireless communication in a variety of applications including traffic management, active safety services, automated tolling, etc. V. DATA C OLLECTION & P ROCESSING Our key vision is that an efficient in-vehicle platform could be devised for enhancing active-safety features in vehiclearea-networks. In this platform, vision-based tools as well as sensors collect important information of the driver/vehicle and car surroundings. Cameras offer a low-cost solution for collecting critical information of the road and surrounding environment. They are also fully non-invasive and transparent to driver. In addition, cameras pointing outside can be widely accepted as there would be no privacy concern. Road data could also be collected using vision-based tools such as cameras or sensors if such infrastructure exists on the road. In this platform, image processing techniques (e.g. MATLAB or customized software modules) can be used to extract useful information and warn/assist driver in hazardous conditions. Vehicle data collection could be sensor-based which is fully transparent to the driver. Sensors would collect the variation of speed, pressure/sequencing of brake/gas pedals, changing pattern of steering-wheel angle, etc. Driver’s physiological data collection can either be camera-based (e.g. for eye closure/blink rate, yawn/chin-drop), or sensor-based for physiological signals such as heart rate, EOG, etc. Embedded microprocessors is needed for in-vehicle processing, and FPGA prototype units can be designated for interfacing and specialized peripherals. After capturing images, or sensing data from the intra-vehicle sensors, behavior analysis is required to detect the existence of any abnormalities. The result of behavior analysis can then be sent to: 1) driver (e.g. early warning of a fast approaching car from behind), 2) a monitoring station (e.g. fleet management), and 3) sharing among users (e.g. road status data exchange among nearby vehicles). The overall concept of the in-vehicle platform for data collection & processing is shown in Figure 1.

C. Vehicle-to-Roadside Infrastructure (V2I) Communication Vehicle-to-road communication for sensing and environmental monitoring is another interesting item in the menu of smart vehicle-area-network research. This platform ultimately enables driver safety by providing the right information at the right time, such as speed limit, and weather condition information collected using various road-side sensors [3]. This platform is capable of automatically informing the driver of hazardous road conditions. Sensed data of road surface (e.g. wet condition of the road surface) can be detected based on processing the polarized light from the road surface with a vision system [3]. Anticollision detection systems based on vehicle and obstacle spacing using adaptive cruise control is another vehicle-toroad-side communication application [3] [20]. V2I communication enables real-time weather/traffic updates for the driver, which ultimately makes the transportation systems more informative. As a main challenge, the role of car radars is critical and its design and implementation should be investigated to particularly improve V2I communication. In addition, since data processing would be from hundreds of nodes, prioritization, buffering/queuing techniques should be devised to maintain a robust and effective data communication link. IV. S TANDARDS

FOR

VAN

Vehicular networks should be designed based upon certain standards which define the communication architecture, protocols, messaging, management, hierarchy, and so on. The main standards for VAN are outlined in this section. • IEEE 802.11p: The IEEE 802.11p standard draft which is still actively under progress, with tentative release date of November 2010, is an amendment to the IEEE 802.11 standard. IEEE 802.11p aims at adding wireless access in vehicular environments (WAVE) to support Intelligent Transportation Systems (ITS) applications [21]. This standard de-

A. Image-Based Processing Image processing is widely used in a large number of applications including engineering and biomedical fields. We envision that a good portion of the data collection in future VANs would be through images and videos captured by the camera. Image-based techniques are also popular because cameras are easy to set up and fully transparent during operation. 3

Figure 1.

Anomaly detection systems can be employed to identify certain actions that may compromise driver/vehicle safety, and to minimize the effect of malicious breaches on VAN [34]. Some approaches cited in the literature continuously monitor the networking flow to identify anomalies and/or malicious attempts [35]. For enabling active safety and security features, vehicle area networks can benefit from a behavior analysis architecture. The main idea is to employ data/packet processing techniques (e.g. packet content inspection such as worm detection [36] [37], or machine learning [38], etc.) for such behavioral analysis. The underlying concept is based on finding repetition properties of certain metrics and generating a profiling curve based on those repetitions within a certain time-frame of analysis. Abnormalities of the profiling curve lie where unusual (i.e. too high or too low) repetition of data metrics are observed [36]. Below are a few places in VAN and smart cars where behavior analysis can be effectively deployed: 1) Driver Profiling: In general, machine learning/classification techniques are required to learn and profile the driver’s behavior [38]. Behavior analysis could provide a driver profile based on the driver’s physiological signals (e.g. heart rate, EOG etc.) and/or vehicle information (speed, GPS routing, tire traction and stability, etc.) collected from various sensors. A profiling curve can be constructed based on the frequencies of metric that could ultimately reflect the abnormal behavior. 2) Fatigue Detection: The fatigue detection engine can analyze and differentiate normal vs. abnormal regions representing the alert vs. non-alert status of a driver by analyzing physiological signals collected from sensors and vision-based data captured by the cameras . This engine is a classifier for detecting driver fatigue by providing a profiling curve of the collected driver/vehicle behavior. 3) Inter-VAN Communication: Images captured from car surroundings/driver can be initially preprocessed to distinguish different objects and their motions/directions from one another. Behavior analysis could then keep track of any metric (e.g. speed of a certain vehicle) that falls outside the normal region for the majority of vehicles in the image. This is extremely useful for V2V and V2I. Behavior analysis and profiling can also assess normalcy of a driver’s behavior, and diagnose a vehicle’s malfunction occurrences, which could be useful for V2C communication.

In-Vehicle data collection & processing platform.

Most image-processing techniques use gray-scale to simplify the processing complexity. Techniques such as edge detection, segmentation, classification, tracking, etc., are used to identify a particular object, or the motion of an object [23]. Applications and processing techniques include the following: • Traffic Sign/Light Reading: Cameras pointing outside can capture images of road signs and traffic-light signs. Imageprocessing techniques can detect these signs with high accuracy. These techniques first detect signs in images using techniques such as color-based [24] or shape-based segmentation [25]. Then, a classification algorithm such as cross correlation or template matching is used to identify/classify the nature of the detected sign [26]. A tracking technique such as Kalman filtering can be used to boost the interpretation accuracy of the signs [27]. • Distance/Speed/Collision Estimation: Another interesting feature of image-based analysis is that the distance, relative speed and acceleration of nearby objects could be estimated through an image/video. This feature first detects new vehicles in the observation field. Then, vehicles with high collision potentials could be found by relative distances. When adding motion analysis to the game, dangerous situations become predictable [28]. Spatial or temporal analysis of image such as knowledge-based (e.g., color, geometrical features, shadow, etc.) and motion-based analysis are used to localize candidate vehicles. Then, template-based correlation or appearancebased techniques verifies the localized vehicles [29]. In addition, Optical flow analysis of images can extract both qualitative (approaching and departing) and relative speed of vehicles located in the observation field [30]. • Lane Detection and Tracking: Lane detection and tracking is another useful application of image-based techniques to predict/warn driver on lane departure, assist in vehicle guidance, etc. [31]. Such analysis requires edge detection along with parameter estimation of lanes using techniques such as mathematical modeling, Hough transform, etc. to extract lane boundaries [32]. After successful lane detection, a tracking algorithm on consecutive frames can be used to complete the detection process. • Driver Facial Analysis: In a VAN platform, cameras not only record the car surrounding images, but also, cameras pointing inside can collect images useful for behavioral analysis. Essentially, action recognition/classification should be used to determine a meaningful description of the action or pattern that may correspond to hazardous conditions (e.g. driver fatigue and sleep episode detection) [33].

B. Language/Protocol & Message Passing After useful/alert/warning information have been extracted, a common language should be devised across the entire vehicle-area-network that would translate the information to this language and broadcast the information to vehicles in nearby proximity. The advantage of using a common language (e.g. a textual expression showing the existence of traffic jam, construction, pedestrian/bicyclist passing, etc.) lies within the power-efficient nature of broadcasting less data, rather than sharing so many images. Such language/message passing protocol should communicate via communication standards that comply with IEEE 802.11p. 4

Applications of such in-vehicle VAN platform can be used for cooperative driving for determining lane detection mechanisms, assisting elderly drivers, and even parking assist. This platform can also be an accurate model for driver fatigue detection, the vehicle’s maintenance status, insurance purposes and network fleet management. Another interesting application of this platform is the fact that it could be used as the car black box [39], providing evidence for claims in accidents/insurance purposes. It also offers personal and public security as the car surrounding is being monitored continuously.

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