Abstract
Driver’s intention of the front vehicle plays an important role in the automatic emergency braking (AEB) system. If the front vehicle brakes suddenly, there is potential collision risk for following vehicle. Therefore, we propose a driver’s intention recognition model for the front vehicle, which is based on the backpropagation (BP) neural network and hidden Markov model (HMM). The brake pedal, accelerator pedal, and vehicle speed data are used as the input of the proposed BP-HMM model to recognize the driver’s intention, which includes uniform driving, normal braking, and emergency braking. According to the recognized driver’s intention transmitted by Internet of vehicles, an AEB model for the following vehicle is proposed, which can dynamically change the critical braking distance under different driving conditions to avoid rear-end collision. In order to verify the performance of the proposed models, we conducted driver’s intention recognition and AEB simulation tests in the cosimulation environment of Simulink and PreScan. The simulation test results show that the average recognition accuracy of the proposed BP-HMM model was 98%, which was better than that of the BP and HMM models. In the Car to Car Rear moving (CCRm) and Car to Car Rear braking (CCRb) tests, the minimum relative distance between the following vehicle and the front vehicle was within the range of 1.5 m–2.7 m and 2.63 m–5.28 m, respectively. The proposed AEB model has better collision avoidance performance than the traditional AEB model and can adapt to individual drivers.
Highlights
Rear-end collisions are the most common traffic accidents, with more than 90% due to drivers’ inattention or nervousness [1]. e National Transportation Safety Board (NTSB) points out that 80% of rear-end collisions can be avoided by using advanced collision avoidance systems [2]
E automatic emergency braking system (AEB) is a typical advanced collision avoidance system, which uses onboard sensors to detect collision risk and automatically brakes when necessary to avoid collision
According to the research report [3], when the vehicle speed is less than 50 km/h, vehicles using the AEB system can reduce the rearend accidents by 38%. erefore, it is of great significance to study the AEB system
Summary
Rear-end collisions are the most common traffic accidents, with more than 90% due to drivers’ inattention or nervousness [1]. e National Transportation Safety Board (NTSB) points out that 80% of rear-end collisions can be avoided by using advanced collision avoidance systems [2].e automatic emergency braking system (AEB) is a typical advanced collision avoidance system, which uses onboard sensors to detect collision risk and automatically brakes when necessary to avoid collision. Erefore, it is of great significance to study the AEB system. It is the keys for the AEB system to judge the dangerous degree and establish the collision avoidance model. Many studies use safety braking distance [4,5,6] or time to collision (TTC) [7,8,9] for risk measurement. Chen et al [11] proposed a new algorithm that considered both time collision and safety braking distance. Kaempchen et al [12] proposed a method to calculate the AEB emergency braking trigger time, which considered all possible trajectories and dimensions of the target and host vehicle.
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