A lane-changing trajectory re-planning method considering conflicting traffic scenarios

An essential aspect of intelligent driving systems is the automatic lane-changing function. However, in real-world traffic situations, the initially planned lane-changing trajectory can become hazardous due to the intricate and unpredictable nature of human driving behavior. Based on the assumption that vehicles have risks during lane-changing, an integrated methodology is proposed to assess the hazards associated with road conditions in real-time and to quickly adjust the predetermined vehicle trajectory, if deemed necessary, to mitigate the risks of conflicting lane changes. Vehicles are encouraged to adhere to lane changing behavior by adjusting their trajectory, aiming to enhance traffic efficiency. Instead of immediately abandoning lane changing, vehicles should strategically assess the situation before making decisions. Initially, an analysis of variables influencing re-planning is conducted, determining the circumstances conducive to maintaining lane-changing behavior. Subsequently, a trajectory re-planning module is introduced, facilitated by two neural network data-fitting models, allowing real-time performance. Finally, a series of numerical experiments confirm that the devised method effectively guides autonomous driving through quick and secure lane change re-planning in high-risk traffic environments. The proposed novel approach extends the capacity to target traffic flow gaps and dynamically re-plan lane switching motivations, ensuring the vehicle can persist in lane-changing rather than reverting to the original lane.