2023/10/27 by Ömer Şahin Taş, Taş, Ömer Şahin, Christoph Stiller +2 · 1 citation
Computer Science · Engineering · #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #FOS: Mathematics #Optimization and Control (math.OC) #Robotic Path Planning Algorithms #Robotics (cs.RO) #Vehicle Dynamics and Control Systems
paper · pdf · doi:10.48550/arxiv.2310.17963
openalex publication_date 2023/10/27 · openalex created_date 2023/11/01 · openalex updated_date 2026/07/28
Continuous optimization based motion planners require specifying a maneuver class before calculating the optimal trajectory for that class. In traffic, the intentions of other participants are often unclear, presenting multiple maneuver options for the autonomous vehicle. This uncertainty can make it difficult for the vehicle to decide on the best option. This work introduces a continuous optimization based motion planner that combines multiple maneuvers by weighting the trajectory of each maneuver according to the vehicle's preferences. In this way, the planner eliminates the need for committing to a single maneuver. To maintain safety despite this increased complexity, the planner considers uncertainties ranging from perception to prediction, while ensuring the feasibility of a chance-constrained emergency maneuver. Evaluations in both driving experiments and simulation studies show enhanced interaction capabilities and comfort levels compared to conventional planners, which consider only a single maneuver.