Adaptive Cruise Control Optimization of Automatic Driving Based on Safety Risk Prediction

Abstract:

This paper， extracting the scenario feature indexes and risk metrics index from vehicle kinematic status parameters and road infrastructure condition parameters， uses the extreme gradient boosting （XGboost） model and the long short-term memory （LSTM） model for safety risk prediction. Then， it proposes an adaptive cruise control （ACC） optimization method of automatic driving based on safety risk prediction. It selects collision probability， average speed， and standard deviation of speed to evaluate the performance of ACC optimization， and verifies the rationality and effectiveness of the ACC optimization method proposed using Prescan-Simulink co-simulation. The results show that the safety risk-based ACC optimization method is superior to the general ACC. Compared with the XGboost， the LSTM as safety risk prediction model， has a better performance for ACC optimization. The addition of road infrastructure condition parameters for safety risk prediction improves the ACC performance and reduces the collision probability of automatic driving significantly.