Unifying Decision Making and Trajectory Planning in Automated Driving through Time-Varying Potential Fields
This addresses the challenge of integrating decision making and planning in automated driving, though it appears incremental as it builds on existing potential field methods with time-varying extensions.
The paper tackles the problem of automated driving by proposing a unified framework for decision making and trajectory planning using Time-Varying Artificial Potential Fields (TVAPFs) that model dynamic obstacles with uncertainty. The approach demonstrated effectiveness in simulation with real road topology, showing advantages in real-time suitability.
This paper proposes a unified decision making and local trajectory planning framework based on Time-Varying Artificial Potential Fields (TVAPFs). The TVAPF explicitly models the predicted motion via bounded uncertainty of dynamic obstacles over the planning horizon, using information from perception and V2X sources when available. TVAPFs are embedded into a finite horizon optimal control problem that jointly selects the driving maneuver and computes a feasible, collision free trajectory. The effectiveness and real-time suitability of the approach are demonstrated through a simulation test in a multi-actor scenario with real road topology, highlighting the advantages of the unified TVAPF-based formulation.