Safe and Efficient DRL Driving Policies Using Fuzzy Logic for Urban Lane Changing Scenarios

Abstract

Lane changing is common in driving. Thus, the possibility of traffic accidents occurring during lane changes is high given the complexity of this process. One of the primary objectives of intelligent driving is to increase a vehicle's behavior, making it more similar to that of a real driver. This study proposes a decision-making framework based on deep reinforcement learning (DRL) in a lane-changing scenario, which seeks to find a driving strategy that simultaneously considers the expected lane-changing risks and gains. First, a fuzzy logic lane-changing controller is designed. It outputs the corresponding safety and lane-change gain weights by inputting relevant driving parameters. Second, the obtained weights are brought into the constructed reward function of DRL. The model parameters are designed and trained on the basis of lane-changing behavior. Finally, we conducted experiments in a simulator to evaluate the performance of our developed algorithm in urban scenarios. To visualize and validate the estimated driving intentions, lane-changing strategies were tested under four scenarios. The results show that the average improvement in travel efficiency in the four scenarios is 19%. In addition, the average accident rate in the four scenarios increased by only 4%. We combine fuzzy logic and DRL reward functions to personify the lane-changing behavior of intelligent driving. Compared with conservative strategies that prioritize only safety, this method can considerably improve the number of lane changes and travel efficiency for autonomous vehicles (AVs) on the premise of ensuring safety. The approach provides an effective and explainable method designed for facilitating intelligent driving lane-changing behavior.