Path-Planning Strategy for Lane Changing Based on Adaptive-Grid Risk-Fields of Autonomous Vehicles

Abstract

The quantification and effective representation of safety risks for scenarios in structured road traffic environments of autonomous driving are currently being investigated in an active way. Based on artificial potential fields, a risk-field model for the traffic environment that considers the motion state of an obstacle vehicle is established, and an adaptive-grid risk-field method is proposed for autonomous vehicles. In this method, the traffic environment is meshed initially, and adaptive-grid division is performed using a quadtree grid-dividing strategy for root grids where the grid risk values are within the division interval, which allows for a more accurate quantification of traffic environment risk values. Adding adaptive-grid risk-field parameters to the cost function of the path-planning algorithm improves the accuracy of path safety risk assessment and completes the evaluation and selection of the optimal lane-change path. Simulation results show that the adaptive-grid risk-field established in this paper can effectively express the safety risks of the traffic environment, and the path-planning algorithm incorporating the adaptive-grid risk-field can obtain better paths for lane change compared with the traditional path-planning algorithm, while ensuring the safety of lane change.