Unmanned driving technologies have great potential in improving traffic safety and reducing driver workload, but the human driving mechanism is rarely considered. Human-like unmanned driving could meet the expectations of passengers and pedestrians, so that unmanned vehicles can be more widely accepted. However, current human-like unmanned driving methods rely heavily on historical data, only imitating the driver's behavior without fundamentally explaining the motivation behind his behavior. This study develops a driver risk perception model that describes drivers' perceptions of risk for providing an in-depth explanation of human driving mechanisms to lay a theoretical foundation for human-like driving methods. It also reveals the characteristics of the driver's perceived risk index during lane changing and its relationship with the lane change trajectory. Finally, it proposes a human-like autonomous lane change trajectory planning method, which achieves autonomous planning of human-like lane change trajectory with high fidelity by using the position and speed information of the ego vehicle and obstacles.
Human-Like Autonomous Lane Change Trajectory Planning Based on Driver Risk Perception Model
2023-10-13
1211956 byte
Conference paper
Electronic Resource
English
Study on Lane Change Trajectory Planning Considering of Driver Characteristics
SAE Technical Papers | 2018
|Study on Lane Change Trajectory Planning Considering of Driver Characteristics
British Library Conference Proceedings | 2018
|Data-driven Trajectory Planning of Lane Change Maneuver for Autonomous Driving
SAE Technical Papers | 2023
|