Risk assessment based collision avoidance decision-making for autonomous vehicles in multi-scenarios

In this paper, we proposed a new risk assessment based decision-making algorithm to guarantee collision avoidance in multi-scenarios for autonomous vehicles. A probabilistic-model based situation assessment module using conditional random field was proposed to assess the risk level of surrounding traffic participants. Based on the assessed risk from the situation assessment module, a collision avoidance strategy with driving style preferences (e.g., aggressive or conservative) was proposed to meet the demands of different drivers or passengers. Finally, we conducted experiments in Carla (car learning to act) to evaluate our developed collision avoidance decision making algorithm in different scenarios. The results show that our developed method was sufficiently reliable for autonomous vehicles to avoid collisions in multi-scenarios with different driving style preferences. Our developed method with adjustable driving style preferences to meet the demand of different consumers would improve drivers' acceptance of autonomous vehicles.