Computation of Driver Safety Rating using In-Vehicle Data Recorder: Case Study of Bangkok Public Transportation Chalermpol Saiprasert and Wasan Pattara-Atikom National Electronics and Computer Technology Center 112 Phahonyothin Road, Klong Neung, Klong Luang District Pathumthani 12120 Thailand Phone +662-564-6900, Fax +662-564-6901 Email:
[email protected],
[email protected]
Abstract— This paper proposes a novel algorithm for the computation of Driver Safety Rating. The Driver Safety Rating is evaluated by detecting dangerous driving patterns based on vehicles data collected via in-vehicle data recorder. Dangerous driving patterns include rapid acceleration, harsh braking and driving over the speed limit and four triggering events are used in the proposed algorithm. A case study using real world data from public mini vans operating throughout Bangkok is deployed in this work. In addition to the proposed algorithm, a fine tuning technique to adjust the calculation process is introduced. An online survey is used to collect passengers feedback in the form of a score to reflect how they actually feel about the safety aspect of the journey. The proposed algorithm is then finely tuned in order to optimise for the scores derived from passengers feedback. Experimental results reveal that only two of the four triggering events have a significant impact on the overall computation of the Driver Safety Rating for this given set of data.
I. I NTRODUCTION Road traffic accidents occur frequently in our everyday life and is one of the leading causes of death in many countries around the world [1]. It is a well known fact that three major causes of road accidents are human errors, road defects and vehicle defects [2]. Hence, one way of reducing the number of road traffic accidents is to minimise human errors. In-vehicle data loggers are on-board devices that record data regarding the control, movement and performance of the vehicle of interest [3]. In recent years a number of invehicle data loggers have been developed with a wide variety of features. The information that is often recorded can be categorised into various domains such as vehicle movement that includes acceleration and speed of vehicle, driver control that includes engine throttle and wheel angle, and state of vehicle safety system that includes air bags and seat belts [4], [5]. An example of such devices can be found in the form of automobile data recorders which are widely utilised in many applications. Vehicle tracking system is one area which makes use of automobile data recorders together with Global Positioning System (GPS). As well as providing a means to track vehicles, data collected from the in-vehicle data recorders are used to compute fuel consumption rates since
there is a relationship between driving characteristics and how much fuel is consumed [6]. In addition to the computation of fuel consumption rates, the characteristics of driving data gathered through in-vehicle data recorder can be analysed in order to detect different driving patterns. These include acceleration, deceleration, rate of turning and lane changing which are the key factors contributing to the detection of dangerous driving [7]. The benefit of being able to detect dangerous driving is the reduction in road traffic accidents. In this paper, a novel methodology to evaluate drivers behaviour based on driving data from in-vehicle data recorders are presented. The evaluation of driver behaviour takes into account the vehicle’s movement and state of vehicle safety systems in order to produce driver safety ratings. Real world data are deployed in this paper where data are collected from data recorders installed in mini vans which is a mode of public transportation in Bangkok Thailand. Moreover, another contribution form this paper is the fine tuning of the driver safety evaluation algorithm using vans passengers score as a reference through a questionnairebased survey. Consequently the final Driver Safety Rating calculation algorithm takes into account passengers feedback factor which is unlike any other approaches in the literature. The paper is organised as follows. Related work in the literature with regards to the use of in-vehicle data recorders is reviewed in section two. The third and fourth sections describe the proposed algorithm focusing on how driver safety rating is calculated. Section five of this paper discusses the fine tuning of the proposed algorithm based on vans passengers feedback. Finally, the last section concludes this paper. II. R ELATED W ORK In vehicle data recorders with the ability to record vehicle’s data are becoming more common in the field of transportation. This is especially true in vehicles used for public transportation and fleet and logistics where it is crucial to be able to track and identify where the vehicle is in real-time. A number of examples exist in the literature with
regards to utilising in-vehicle data recorders and the data they collect to evaluate driving behaviour. Lotan and Toledo proposed a system called DriveDiagnostic in order to record and analyse drivers behaviour for crash and pre-crash events [8]. In addition, a safety index is calculated based on the collected data. However, there is no clear explanation of how the index was derived. The data from these data recorders can provide us with very useful information once it is properly analysed. An example of this is the work in [9], where a correlation of car accidents and the risk index obtained from collected driving data is established. Ueyama et al collected and monitored pre and post accidents data from data recorders installed in vehicles in Japan [10]. Their findings reveal that there is a relationship between driving behaviour and the likelihood of accidents. As well as longitudinal data recorded using the in vehicle recorders, video and voice technology have also been considered. Arai et al incorporated video sensor to the already existing data from the recorder in order to reconstruct accidents in greater detail [11]. In addition to visual data, Green et al introduced the use of audio sensors including horns, clashing metal sounds and squealing tires for the analysis of crashes at an intersection [12]. The objective of [12] is to detect patterns of accidents for relevant improvements. Han and Yang’s work discuses an analysis of driver’s characteristic for the detection of dangerous driving using data from automobile data recorder [7]. Dangerous driving actions that lead to reckless driving were clearly defined with detailed description of the algorithms to detect each event. However these data and analysis were not transformed into an index or a rating in order to provide a measure of driver’s behaviour. All of the approaches mentioned earlier are missing a key ingredient which is the computation of driver safety rating in a form of a measurement to gauge how safe the driver is based on the detection of dangerous driving patterns. The algorithm proposed in this paper provides a solution to that problem. In addition, a fine tuning technique to adjust the algorithm to optimise for passengers feedback based on scores given in an online survey is introduced. As a result, the proposed algorithm models this passengers feedback factor into the calculation of Driver Safety Rating in order to best reflect actual events occurring during the journey. This is an issue which has not previously been considered in other approaches in the literature. III. OVERALL F RAMEWORK Figure 1 presents the overall framework of this paper. It can be divided into two parts, namely the vehicle module and the data processor module. The main function of the vehicle module is to collect raw driving data from two main sources using a commercially available in-vehicle data recorders. The first set of data such as vehicle’s speed and acceleration are gathered from in-vehicle sensors. The other set of data are collected from the built-in GPS receiver resulting in vehicle’s
GPS data
Data Processing Module Database Driver Safety Ra2ng Evalua2on Algorithm
Raw data
Vehicle Sensors
Vehicle Module
Fig. 1.
Driver Safety Ra2ngs
Overview of the proposed framework
positioning data. These two sets of data are sent to the data processor module via 3G connection to be analysed. After receiving raw data from the vehicle module, the second module of the system called the data processing module takes a stream of raw data and passes it through our proposed algorithm in order to obtain an index called a Driver Safety Rating. In this work the Driver Safety Rating is defined as a measurement to evaluate how safe the driver is based on driving data collected from our vehicle module. A. Driving Data As mentioned in the section I, an in-vehicle data recorder can provide a wide range of data depending on the type of sensors being installed on-board. The sensors used in this work are mounted in the in-vehicle recorder. The automobile data recorders deployed in this paper are installed in public mini vans which operate throughout Bangkok. The following items are the type of data collected with their corresponding description. • Speed - a measurement of instantaneous speed at which the vehicle is travelling at for each data sample. • Position - a geographical location of the vehicle described by the latitude and longitude for each instance of a data sample. • Heading - a measure of direction of travel in degrees East of True North. • Rapid Acceleration - this will be true when rapid acceleration condition is detected otherwise it will be false. • Harsh Braking - this will be true when harsh braking condition is detected otherwise it will be false. • Rapid Turning - when this is true the data gathering rate changes to one sample per second. • Door Status - status of the van’s door where it is marked as true when open and false when closed. Data is measured and recorded at a sampling rate of one sample per minute. An exception occurs when rapid turning is detected where the data gathering rate changes from one per minute to one every second.
IV. D RIVER S AFETY R ATING C OMPUTATION A LGORITHM In this section the proposed algorithm deployed to calculate the Driver Safety Rating is introduced. The ultimate goal of the proposed methodology is to assess the behaviour of the driver using the Driver Safety Rating taking into account the raw driving data. Based on the characteristic of driving data collected from the vehicle’s data recorders, four events regarding dangerous driving can be deduced in order to classify and compute the Driver Safety Rating. These are as follows: • Event 1: Rapid acceleration and harsh braking Rapid acceleration occurs when an increased change in speed is greater than a certain value over a specified period of time. On the other hand, harsh braking is the opposite of rapid acceleration and occurs when a decreased change in speed is greater than a certain value over a specified period of time. For both cases, due to the sudden change of speed in a short period of time, reaction time becomes limited. This greatly reduces the driver’s ability to react to any unexpected incidents while driving resulting in possible dangerous collisions [7]. The in-vehicle data recorder used in this paper are able to detect both rapid acceleration and harsh braking. • Event 2 : Vehicle moving while door being open For a mode of public transportation that carries many passengers, it is very dangerous for a van to leave its door open while moving. Data from vehicle’s data recorder provides a status of the vehicle’s door whether it is open or closed. In order to detect dangerous driving we monitor the speed of the van when the status of the door is open. • Event 3 : Speed at which vehicle is turning In the case when the angle at which the vehicle is heading changes more than 30 degrees within a specified period of time, we monitor the speed at which the vehicle is traveling. • Event 4 : Speed on normal road stretch In the case that the collected data sample does not fall into any of the previous Events, it is classified as Event 4 where the speed at which the vehicle is traveling is verified against the speed limit of a public transport van carrying more than 6 passengers which is set to 80km/h in this work. In this paper, the overall Driver Safety Rating SR is defined as in (1).
α4 respectively. N1 is the number of occurrences of Event 1 during a journey. Similarly, N2 , N3 and N4 denote the number of occurrences of Events 2, 3 and 4 respectively. In the case of Event 1, for a journey that lasts from t1 = 1 until t1 = N1 , the score for each Event is calculated for every sample recorded at time t1 = 1 until t1 = N1 . After that the average of the score for each Event is calculated. Note that the number of occurrence of each Event, is likely to be different for all Events as each driver will have a different driving behaviour contributing to different conditions. This means that N1 , N2 , N3 and N4 are likely to have different values. In the next part of this section, the calculation of the score for each Event is described in more detail. A. Calculation of S1 S1 is the score associated with Event 1 when either rapid acceleration or harsh braking is detected. A data sample at each instance t1 will score a “1” unless either rapid acceleration or harsh braking is detected where the score will be zero. This is shown in (2). S1t1 = 1, Event1 = F alse S1t1
(2)
= 0, Event1 = T rue
B. Calculation of S2 S2 is the score associated with Event 2 when we check to see if the vehicle is moving while the door is open. A score of “1” is given when the door of the van is open and the value of speed recorded is equal to zero. On the other hand, the score will be zero if the van violates this condition, i.e. door open and value of speed is non-zero. The score will be “1” when the status of the door is closed. This is shown in (3) S2t2 = 1 , DooropenAN DSpeed = 0 S2t2 S2t2
=1
(3)
, Doorclosed
= 0 , DooropenAN DSpeed 6= 0
C. Calculation of S3 S3 is the score given to the scenarios in Event 3. The speed limit at which a vehicle should be travelling while making a turn is 30km/h [7]. As a result, the scoring scheme for Event 3 is given below in (4) S3t3 = 1, vd ≤ 0 S3t3
(4)
= max(1 − 0.05vd3 , 0), vd3 > 0 vd = vactual3 − vlimit3 ,
(5)
where vd3 denotes the difference between actual vehicle’s speed (vactual3 ) and the speed limit (vlimit3 ). From Figure 2 the value of S3 is one up until the speed value is N1 N2 N3 N4 α2 X α3 X α4 X α1 X t1 t2 t3 t4 greater than the specified speed limit of 30km/h. After this S + S + S + S , SR = N1 t =1 1 N2 t =1 2 N3 t =1 3 N4 t =1 4 point the score S3 linearly decreases as the speed increases 1 2 3 4 (1) until the vehicle’s speed is above 50km/h, after which the where SR denotes the overall Driver Safety Rating. S1t1 score S3 becomes zero. This subsequently allows for 67% denotes the score based on Event 1 at time t1 while α1 discrepancies on top of the speed limit since making a turn at denotes the weighting associated with this event. Similar the speed above 50km/h is considered to be reckless driving. notation applies to S2t2 , S3t3 and S4t4 . Likewise the weighting As this is our initial approach we have decided to model S3 associated with Event 2, 3 and 4 are denoted by α2 , α3 and linearly.
5
4.5 S = 1−0.05v
S3
1
d
Driver Safety Rating
3
0.5 0
4
3.5
3
2.5 −40 −30 −20 −10 0 10 20 30 Difference in Speed vd (km/h)
Fig. 2.
40
2 0
S3 scoring model
S4
0.5
0
Fig. 3.
S4 scoring model
S4 is the score given to the scenarios in Event 4. The speed limit at which a vehicle should be travelling on a normal stretch of road is 80km/h. As a result, the scoring scheme for Event 4 is given below in (6) S4t4 = 1, vd4 ≤ 0
30
40 50 Time (mins)
60
70
80
Time Series of Driver Safety Rating
V. F INE T UNING OF P ROPOSED A LGORITHM
40
D. Calculation of S4
S4t4
20
Van A, denoted by a solid line, made the journey in the morning. On the other hand, Van B which is denoted by a dotted line made the same journey but in the evening. It can be seen from the plot in Figure 4 that the two time series follow similar pattern but there are many instances where the values of SR are different. This is due to many factors including different driving behaviour and the level of traffic congestion at the time of the journey.
1
−20 0 20 Difference in Speed vd (km/h)
10
Fig. 4.
S4 = 1−0.025vd
−40
Van A Van B
(6)
= max(1 − 0.025vd4 , 0), vd4 > 0,
where vd4 denotes the difference between vehicle’s speed and the speed limit. Similar to the calculation of S3 , the value of S4 is one up until the speed value is greater than the specified speed limit of 80km/h. After this point the score S4 linearly decreases as the speed increases until the vehicle’s speed is above 120km/h, after which the score S4 becomes zero. This subsequently allows for 50% discrepancies on top of the speed limit. The scoring model of S4 is depicted in Figure 3. E. Overall Rating SR The overall safety rating SR is calculated using the expression in (1), which is the sum of all of the score from the four Events. Figure 4 presents an example of a Driver Safety Rating breakdown in the form of a time series. This particular example illustrates a comparison of two mini vans making exactly the same journey but at a different times of day. The entire journey lasts 75 mins. The Driver Safety Rating has been scaled up to have a maximum value of 5.
So far this paper has presented a novel algorithm to calculate Driver Safety Rating of Bangkok’s mini vans based on data collected through in-vehicle data recorders. The mathematical expression is presented in (1). At this stage, the weighting (α) of the four Events have been set equally to one. Thorough statistical data analysis was performed on the collected data and it was found that the number of occurrences of Events 1,2,3 and 4 are randomly distributed with some Event occurring more regularly than others over the time step for each trip. Hence, a more appropriate weightings should be given to each of the four Events in order to best reflect the driver’s actual driving behaviour. The main purpose of the experiment carried out in this section is to compute for the optimum values of weighting α for the four Events using passengers feedback as our reference. A. Set-up An online survey was used to collect passengers feedback based on the safety aspect of public mini vans drivers. Passengers are asked to complete the online survey during the trip. The route chosen to be a case study in this paper is van number T85 which operates from central Bangkok to Thammasart University Rangsit campus. An example of a public mini van is shown in Figure 5. Figure 6 presents the route map of mini van under investigation. Point A on the map denotes Thammasart University Rangsit Campus while Point B denotes Victory Monument in Central Bangkok. The 45km long route is divided into 3 main segments. Segment 1 is part of a highway going through the outskirt of Bangkok while Segment 2 is an elevated tollway built for commuters traveling in and out of the city. Finally Segment 3 is the
B. Analysis TABLE I S CORES FROM PASSENGERS F EEDBACK AND P ROPOSED A LGORITHM .
Fig. 5.
Public Mini Van in Bangkok
Fig. 6. Mini Van Route under Investigation, Source: www.maps.google.com
city zone. The reason that the overall route is divided into 3 segments is that the three segments contain types of road with different characteristics. For example, Segment 2 is expected to have the highest traffic flow rate while Segment 3 is expected to be the most congested area. In this experiment data was collected over a period of 3 months from July until September 2011 from 40 different mini vans with 25 drivers with varied driving behaviour. At full capacity the mini vans can take 12 passengers with an average of 6 passengers per trip. In the survey passengers are asked to score the driver in each of the three route segments based on how they feel in terms of the safety aspect with the maximum score being 5. In total 100 responses were received. The 100 responses were cross referenced with the vehicle data collected from data recorders and it was found that 15 out of 100 passengers feedback matched our vehicles’ data. The low number of matching cases is due to an error in obtaining data from data recorders. These 15 cases will be utilised for the fine tuning of the algorithm in the next section.
Seg1 4 3 4 4 5 4 3 4 4 5 5 4 3 5 5
Passengers Score Seg2 Seg3 5 5 3 3 3 4 4 4 5 5 1 4 3 3 5 5 4 5 4 4 4 4 3 2 3 3 4 4 5 5
Sp 4.67 3.00 3.67 4 5 3 3 4.67 4.33 4.33 4.33 3 3 4.33 5
S1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
Proposed Algorithm S2 S3 S4 5 3.28 5 5 3.93 4.07 5 3.22 4.68 5 4.48 4.88 5 3.39 4.21 5 3.82 4.21 5 4.45 4.92 5 4.62 4.70 5 4.18 4.70 5 3.99 4.77 5 3.01 4.15 5 2.44 3.88 5 3.72 5 5 5 5 5 3.89 3.92
SR 4.57 4.50 4.47 4.84 4.40 4.51 4.84 4.83 4.72 4.69 4.29 4.08 4.68 5 4.45
Table I presents the scores obtained from passengers feedback as well as the scores calculated from the corresponding vehicle’s data using the proposed algorithm, where Seg1, Seg2 and Seg3 denote Segments 1, 2 and 3. Sp denotes the overall passengers score which is the average score for that journey based on the scores from Segments 1, 2 and 3. Each row in the table represents the scores that originate from the same journey. The scores in the first four columns of each row are from passengers feedback while the last five columns are calculated from the proposed algorithm using raw data from in-vehicle data recorders. The values of SR in Table I is calculated using the same weightings for all Events, i.e., α1 = α2 = α3 = α4 . It can be seen from Table I that the values of Sp and SR are quite different. This suggests that the values of α1 , α2 , α3 and α4 are still not fully optimised. C. Multiple Regression and ANOVA Tests In order to find the optimum values of α for this given set of data two statistical processes called Multiple Regression and Analysis of Variance (ANOVA) test are used. In this paper we have a dependent variable SR and four independent variables S1 , S2 , S3 and S4 . Multiple Regression finds the best relationship between the dependent and independent variables. Hence, it allows us to predict a value on one variable on the basis of the correlation of several other variables [13]. The Multiple Regression formulation is expressed in (7). ANOVA test lets us know if there is a statistically significant difference between the means of several groups of samples based on group variances and sample sizes [13]. SR = b0 + b1 S1 + b2 S2 + b3 S3 + b4 S4 ,
(7)
where b1 is the coefficient which indicates how much of an impact S1 has on SR. Similarly, b2 , b3 and b4 are the impact coefficients of S2 , S3 and S4 respectively. b0 is the estimated intercept on the y-axis (SR).
The data set used in Multiple Regression are taken from Table I where S1 , S2 , S3 and S4 are the independent variables. In addition, the score from passengers feedback Sp is used as our desired Driver Safety Rating SR . TABLE II M ULTIPLE R EGRESSION R ESULTS . b0 1.474
b1 0
b2 0
b3 -0.116
b4 0.617
From Table II, statistical analysis through Multiple Regression and ANOVA tests revealed that for the given set of data, S1 and S2 do not have a significant effect on the computation of SR as the values of coefficients b1 and b2 are zero. On the other hand, S3 has a negative impact while S4 has a positive impact on SR. Taking that into account, equation (1) can be rewritten as in (8).
SR = 1.474 −
N3 N4 0.617 X 0.116 X S3t3 + S t4 , N3 t =1 N4 t =1 4 3
of weightings are computed for the significance of each of the four events in order to optimise for the scores derived from passengers feedback. Multiple Regression and ANOVA test results demonstrate that, for this given data set, rapid acceleration and harsh braking and state of the van’s door while it is moving do not have a significant impact on the calculation of the Driver Safety Rating. Thus, there is a strong relationship between the Driver Safety Rating and the speed at which vehicle is turning and vehicle’s speed on a normal road stretch. The reason for this might be that the first two events rarely occurred within the data set that were used in this paper. As a result, the correlation between S1 , S2 and SR becomes very minimal. Future work for this work includes a generic algorithm to work out Driver Safety Rating for all types of vehicles. In order to achieve that, more data from in-vehicle data recorders from other means of transportation must be gathered and analysed. As a result, more events to classify dangerous driving are expected to be established. Hence, a more generic algorithm can be obtained.
(8)
4
where the coefficients b3 and b4 are used as the weighting values of α3 and α4 . This ensures that the model in (8) is able to accurately produce the Driver Safety Rating of mini van drivers based on in-vehicle recorder data. In summary, Multiple Regression and ANOVA test results demonstrate that, for this given set of data, rapid acceleration and harsh braking and state of the van’s door while it is moving do not have a significant impact on the calculation of the Driver Safety Rating. Thus, there is a strong relationship between the Driver Safety Rating and the speed at which vehicle is turning and vehicle’s speed on a normal road stretch. The reason for this might be that Events 1 and 2 rarely occurred within the data set that were used in this paper. As a result, the correlation between S1 , S2 and SR becomes very minimal. VI. C ONCLUSION In conclusion, this paper proposes a novel algorithm to calculate Driver Safety Rating, which is a measure of how safe the driver’s behaviour is, for public mini vans operating throughout Bangkok area. The Driver Safety Rating is calculated by detecting dangerous driving patterns based on vehicles data collected via in-vehicle data recorders. Dangerous driving patterns include rapid acceleration, harsh braking and driving over the speed limit. Four categories relating to driver behaviour are deployed in this work as a means to calculate the Driver Safety Rating. Hence, by combining the physical and positional data gathered, we are able to compute a rating to measure driver’s behaviour. In addition to the proposed algorithm, a fine tuning technique to adjust the computation process is introduced. An online survey is used to collect passengers feedback in the form of a score to reflect how they actually feel about the journey in terms of safety aspect. Appropriate values
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