Treffer: Performance evaluation and comparison of piecewise search, shooting heuristic, and dynamic programming for multi-vehicle trajectory optimization.

This paper investigates multi-vehicle trajectory optimization in complex traffic scenarios, including non-signalized intersections and signal-controlled road segments. Three optimization models are evaluated: Piecewise Search Algorithm (PSA), Shooting Heuristic (SH), and Dynamic Programming (DP). To improve computational efficiency without sacrificing quality, we propose a leader-based strategy, optimizing only key vehicles while others follow car-following rules. Experiments on simulated and real-world data show reductions in Vehicle Specific Power (VSP) by 4–19% and computation time by over 90%. Results highlight PSA's superiority in efficiency for real-time applications, SH's in energy savings, and DP's in robustness at higher computation costs. The leader-based approach enhances these advantages, supporting scalable deployment in large-scale, heterogeneous environments. Robustness is validated under stochastic driving behaviors and extended to multi-objective optimization of fuel consumption and trajectory smoothness. Practical implications are demonstrated in ramp merging and highway entrances, providing guidance for selecting tailored strategies in diverse traffic conditions. [ABSTRACT FROM AUTHOR]

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