Abstract
Surrogate Safety Measure (SSM) is one of the most widely used methods for identifying future threats, such as rear-end collision. Various SSMs have been proposed for the application of Advanced Driver Assistance Systems (ADAS), including Forward Collision Warning System (FCWS) and Emergency Braking System (EBS). The existing SSMs have been mainly used for assessing criticality of a certain traffic situation or detecting critical actions, such as severe braking maneuvers and jerking before an accident. The ADAS shows different warning signals or movements from drivers’ driving behaviours depending on the SSM employed in the system, which may lead to low reliability and low satisfaction. In order to explore the characteristics of existing SSMs in terms of human driving behaviours, this study analyzes collision risks estimated by three different SSMs, including Time-To-Collision (TTC), Stopping Headway Distance (SHD), and Deceleration-based Surrogate Safety Measure (DSSM), based on two different car-following theories, such as action point model and asymmetric driving behaviour model. The results show that the estimated collision risks of the TTC and SHD only partially match the pattern of human driving behaviour. Furthermore, the TTC and SHD overestimate the collision risk in deceleration process, particularly when the subject vehicle is faster than its preceding vehicle. On the other hand, the DSSM shows well-matched results to the pattern of the human driving behaviour. It well represents the collision risk even when the preceding vehicle moves faster than the follower one. Moreover, unlike other SSMs, the DSSM shows a balanced performance to estimate the collision risk in both deceleration and acceleration phase. These research findings suggest that the DSSM has a great potential to enhance the driver’s compliance to the ADAS, since it can reflect how the driver perceives the collision risks according to the driving behaviours in the car-following situation.
Highlights
Rear-end collision is one of the most frequent traffic accidents on the roads
This study aims to analyse and compare the collision risks estimated by different Surrogate Safety Measure (SSM) based on two different car-following theories, such as action point model and asymmetric driving behaviour model
In order to investigate the relationship between collision risk and human driving behaviour, this study analyzes three different SSMs based upon two different car-following theories, including the action point model and asymmetric driving behaviour model
Summary
Rear-end collision is one of the most frequent traffic accidents on the roads. Common contributing factors for the rear-end crashes include driver’s inattention and human misjudgments on the amount of required deceleration in car-following situation. In efforts to prevent the rear-end crash and improve vehicular safety, drivers’ judgments must be assisted and guided based on current or upcoming traffic situations For such matter, various Advanced Driver Assistance Systems (ADAS) such as Forward Collision Warning System (FCWS) and Emergency Braking System (EBS) have been developed based on different data sources, including camera, radar, LIDAR, GPS, and connected vehicle network. The kinematic approach-based SSM is to estimate the rear-end collision risk based on the difference between the required stopping distances of two consecutive vehicles. There is a need for exploring the characteristics of existing SSMs in terms of such inconsistency For such purpose, this study aims to analyse and compare the collision risks estimated by different SSMs based on two different car-following theories, such as action point model and asymmetric driving behaviour model. Brief concluding remarks are provided in the last section
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