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Modeling vehicle-pedestrian interactions in the road environment is essential to develop pedestrian detection and pedestrian crash avoidance systems. In this paper, one novel approach is proposed to estimate the vehicle-pedestrian encountering risk in the road environment based on a large scale naturalistic driving data collection. Considering the difficulty to record actual pedestrian crashes in the naturalistic data collection, the encountering risk is estimated by the chances for driver to meet with pedestrian in the roadway as well as the chances for the driver and pedestrian to get into a potential conflict. Effects of different scenarios consisting of road conditions, pedestrian behaviors, and pedestrian numbers on the risk levels are also evaluated, and significant results are provided.

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As active safety systems have been introduced to passenger vehicles, there is an immediate need to develop a standardized testing protocol and scoring mechanism which enables an objective comparison between the performance of active safety systems implemented across various vehicle platforms. This project proposes a methodology for the establishment of such standards to evaluate and compare the performance of Crash Imminent Braking (CIB) systems. The proposed scoring mechanism is implemented based on track testing data in the evaluation of a 2011 model year passenger vehicle equipped with a CIB system.

Vehicle collision avoidance system (CAS) is a control system that can guide the vehicle into a collision-free safe region in the presence of other objects on road. Common CAS functions, such as forward collision warning and automatic emergency braking, have recently been developed and equipped on production vehicles. However, these CASs focus on mitigating or avoiding potential crashes with the preceding cars and objects. They are not effective fo1• crash scenalios with vehicles from the rear-end 01• lateral directions. This paper proposes a novel collision avoidance system that will provide the vehicle with all around (360-degree) collision avoidance capability. A lisk evaluation model is developed to calculate potential lisk levels by considering sunounding vehicles (according to their relative positions, velocities, and accelerations) and using a predictive occupancy map (POM). By using the POM, the safest path with the minimum lisk values is chosen from 12 acceleration based trajecto1•y directions. The global optimal trajectory is then planned using the optimal rapidly explo1•ing random tree (RRT*) algolithm. The planned vehicle motion profile is generated as the reference for future control. Simulation results show that the developed POM-based CAS demonstrates effective operations to mitigate the potential crashes in both lateral and rear-end crash scenalios. I. IN1RODUCTIONAutonomous vehicles (AVs) have become a popular research area in both the automotive industty and academia with the objective of minimizing risks and enhancing safety and comfort. Based on the National Highway Traffic Safety Administt•ation (NHTSA), smashing into the rear of the car ahead is the top cause of vehicle accidents, contt-ibuting up around 30% of all tt•affic accidents annually [I]. Collision avoidance system (CAS) is one of vehicle active safety technologies for dealing with both lane-departure and fo1ward-collision problems, which has been designed and implemented on some production vehicles. D. Sam [2] concluded that most road accidents occured due to human error, and over 90% of those accidents were caused by visual information acquisition problems. However, most of the currently developed CASs were designed to mitigate crashes based on the e1rnrs caused by the ego vehicle (e.g., driver distt•action and drowsiness [3]) and static objects on road, such as lane markings, road edges, and parked

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