A Novel Lane-Change Decision-Making With Long-Time Trajectory Prediction for Autonomous Vehicle - Xudong Wang; Jibin Hu; Chao Wei; Luhao Li; Yongliang Li; Miaomiao Du (IEEE Access, 2023)
In the process of autonomous vehicle lane changing, a reliable decision-making system is crucial for driving safety and comfort. However, traditional decision-making systems have short-term characteristics, which makes them susceptible to real-time inference from surrounding vehicles. Usually, system sacrifices driving comfort to ensure the safety of the lane change. Balancing driving safety and comfort has always been a research challenge. Long-term trajectory prediction can provide accurate future trajectories of target vehicles, providing reliable long-term information to compensate for the short-term variability of decision systems. This paper proposes a novel decision-making model with long-term trajectory prediction for lane-changing. First, we constructed a long-term trajectory prediction model to predict the trajectories of surrounding vehicles. Besides, we built a lane change decision-making model based on fuzzy inferencing, considering the predicted trajectories to infer the relative relationship between other vehicles and the self-driving car. The establishment of the fuzzy rule library considered the vehicle speed, acceleration, system delay time, driver delay time and the distance between vehicles. Finally, we created a dataset for training and testing the trajectory prediction model, and we built 4 cases simulation environments, for two or three vehicles on a straight road or curved road, respectively, to test the decision-making model. Experimental results show that our proposed model can ensure driving safety and improve driving comfort.