The field of autonomous driving is rapidly advancing, with a focus on ensuring the safety and efficiency of vulnerable road users. Recent developments have led to the creation of innovative frameworks and methods for trajectory planning, collision avoidance, and risk assessment. These advancements have the potential to significantly improve the safety and comfort of autonomous vehicles in dynamic and unpredictable environments. Notable papers in this area include: The Safe and Efficient Lane-Changing for Autonomous Vehicles paper, which proposes an improved double quintic polynomial approach for safe and efficient lane-changing in mixed traffic environments. The Risk-aware Spatial-temporal Trajectory Planning Framework for Autonomous Vehicles paper, which presents an enhanced QP-MPC-based framework that incorporates a novel cost function designed with a dynamic hazard field.
Advancements in Autonomous Vehicle Safety and Trajectory Planning
Sources
Vehicle-in-Virtual-Environment (VVE) Method for Developing and Evaluating VRU Safety of Connected and Autonomous Driving with Focus on Bicyclist Safety
Safe and Efficient Lane-Changing for Autonomous Vehicles: An Improved Double Quintic Polynomial Approach with Time-to-Collision Evaluation
A Risk-aware Spatial-temporal Trajectory Planning Framework for Autonomous Vehicles Using QP-MPC and Dynamic Hazard Fields
Toward a Holistic Multi-Criteria Trajectory Evaluation Framework for Autonomous Driving in Mixed Traffic Environment
Safety-Critical Multi-Agent MCTS for Mixed Traffic Coordination at Unsignalized Roundabout
Parameter Tuning Under Uncertain Road Perception in Driver Assistance Systems
Avoidance of an unexpected obstacle without reinforcement learning: Why not using advanced control-theoretic tools?
Adaptive Evolution Factor Risk Ellipse Framework for Reliable and Safe Autonomous Driving