Safer Conflict-Based Search: Risk-Constrained Optimal Pathfinding for Multiple Connected and Automated Vehicles

Coordinating connected and automated vehicles (CAVs) poses challenges in achieving safe and optimal pathfinding simultaneously. In this paper, we propose safer conflict-based search (Safer-CBS), an approach based on conflict-based search that generates risk-constrained optimal paths for multiple CAVs. Safer-CBS addresses these challenges by incorporating risk assessment and optimization techniques. By defining the conflict as the risk between two vehicles exceeding a predetermined threshold, the resultant conflict-free paths can provide safety guarantees. An optimization over risk constraints is devised to enhance the efficiency of the tree search. Additional improvements are also incorporated to expedite the search process. Theoretical analysis demonstrates the optimality of Safer-CBS in finding risk-constrained paths. Furthermore, Safer-CBS can seamlessly integrate trajectory planning to generate risk-constrained trajectories for CAVs. Numerical calculations on grid maps validate the effectiveness of the proposed optimization and additional improvements, resulting in increased success rates (by over 43% for 10 vehicles) and reduced runtime (by over 59%) when compared to the method without the optimization and improvements for Safer-CBS. Simulations in both unstructured and structured environments confirm that Safer-CBS, combined with trajectory planning, ensures that trajectory risks strictly remain below predetermined thresholds, while achieving a superior balance between safety and efficiency compared to the benchmarks.
Note to Practitioners
—Applying multi-CAV technologies in areas such as warehousing, transportation, and operations typically requires the planning of cooperative paths or trajectories for the CAVs. In this planning process, safety and efficiency issues are centrally considered and balanced. Cooperative performance can be ensured as long as each individual vehicle adheres to the central instructions. This study develops a method for risk-constrained cooperative planning, aiming to generate optimal paths and suboptimal trajectories for multiple CAVs. The output trajectories consist of path points with fixed time intervals, which the vehicle controllers can track accordingly. By maintaining the risk level among the vehicles below predetermined thresholds, overall safety can be achieved. Meanwhile, the planning process is optimized, thus improving overall efficiency.