LC-Dual: Coupling predictive information and redundant strategies for autonomous vehicle lane change trajectory planning

Autonomous vehicles (AVs) face considerable challenges when interacting with their environment, especially during lane changes. Enhancing decision-making and planning processes with prediction regarding the intentions and trajectories of surrounding vehicles can significantly improve lane change performance. However, prediction accuracy is limited by both the technology used and environmental variability. Utilizing low-confidence predictive data can adversely impact the safety and comfort of AV operations. This paper proposes a novel dual-model framework for lane change decision-making and planning of AVs (LC-Dual), which dialectically uses predictive data and provides redundant safety measures in parallel. In Model I, the optimal end-state trajectory for lane changes is planned using predictive information from the upper layer. In Model II, a redundant lane change trajectory is quickly generated based on a spatio-temporal safety corridor constructed from real perception data. Ultimately, the selection of the lane change model and trajectory is determined by rule-based decision-making, factoring in prediction confidence and computational efficiency. Simulation experiments demonstrate that the LC-Dual framework yields more adaptive trajectories in scenarios with accurate predictions and effective switches between lane change models in cases of inaccurate predictions. The LC-Dual framework markedly improves safety and efficiency in lane change operations, thereby facilitating broader AV adoption.