Safety Analysis Based on Critical Scenarios and Collision Avoidance of Highly Automated Vehicle

Automated driving is one of the important trends in the automotive industry. In this trend, safety of automated driving vehicles is one of the most important factors to reduce the driving-related collision and improve people's feeling of road traffic safety. For highly automated vehicle, system performs more complex driving tasks and has more interaction with other traffic participants, which brings considerable challenges to identifying safety critical scenarios leading hazards. In this paper, we propose a methodology which provides a solution on how to generate safety critical scenarios from real-world traffic data and how to use them in the process of functional safety analysis. Also, collision models of safety critical scenarios are established for the development and improvement of the automatic driving functions. On one hand, the increasing automation of driving functions can ease the user's driving tasks, on the other hand, it is also easy to confuse the user about the behavior of the vehicle. In other words, once the vehicle appears unexpected behavior, the driver can hardly control the vehicle to avoid collision. For car-following scenarios, backward collision warning and anti-collision lane changing are two effective measures to control the risk and reduce the damage. In addition to considering the safety mechanism of the vehicle, other reasonable external safety measures are easily overlooked but equally important.