Research on Autonomous Vehicle Obstacle Avoidance Path Planning with Consideration of Social Ethics

Abstract

Self-driving car research can effectively reduce the occurrence of traffic accidents, but when encountering sudden people or obstacles cutting into the lane, how to reduce the damage hazard to traffic participants and make ethical decisions is the key point that the development of self-driving technology must break through. When faced with sudden traffic participants, self-driving vehicles need to make ethical decisions between ramming into the traffic participants or other obstacles. Therefore, in this paper, we propose a model decision planning method based on multi-objective evaluation function path evaluation of local path planning. This method addresses the ethical model disagreement problem of self-driving vehicles encountering traffic participants and other obstacles. The aim is to ensure the safety of the lives of the traffic participants and achieve the vehicle’s reasonable ethical decision planning. Firstly, when anticipating traffic participants and other obstacles, the vehicle’s planning intention decisions are obtained through fuzzy algorithms. Different sets of curves for various positions are generated based on dynamic programming algorithms. These curves are then fitted using B-spline curves, incorporating obstacle collision costs, and classifying obstacles into different types with varying cost weights. Secondly, factors such as path length and average path curvature are considered for path total cost calculations. Finally, a local path that avoids traffic participants is obtained. This path is then tracked using a pure pursuit algorithm. The proposed algorithm’s effectiveness is verified through simulation experiments and comparative analyses conducted on the MATLAB platform. In conclusion, this research promotes a safer and more sustainable transport system in line with the principles of sustainable development by addressing the challenges associated with safety and ethical decision making in self-driving cars.