Optimization-based motion primitive automata for autonomous driving
Trajectory planning for autonomous cars can be addressed by primitive-based methods, which encode nonlinear dynamical system behavior into automata. In this paper, we focus on optimal trajectory planning. Since, typically, multiple criteria have to be taken into account, multiobjective optimization problems have to be solved. For the resulting Pareto-optimal motion primitives, we introduce a universal automaton, which can be reduced or reconfigured according to prioritized criteria during planning. We evaluate a corresponding multi-vehicle planning scenario with both simulations and laboratory experiments.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous DrivingMultiobjective OptimizationTrajectory PlanningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Risk-Aware Motion Planning with Learned Trajectory Primitives and Probabilistic Safety Assessment
This paper presents a radial basis function network (RBFN)-informed motion planning framework for safe and efficient urban autonomous driving. The proposed approach combines RBFN-based candidate trajectory generation wit…
Autonomous DrivingMotion PlanningMP-RBFN: Learning-based Vehicle Motion Primitives using Radial Basis Function Networks
This research introduces MP-RBFN, a novel formulation leveraging Radial Basis Function Networks for efficiently learning Motion Primitives derived from optimal control problems for autonomous driving. While traditional m…
Autonomous DrivingMotion PlanningMotion planning for off-road autonomous driving based on human-like cognition and weight adaptation
Driving in an off-road environment is challenging for autonomous vehicles due to the complex and varied terrain. To ensure stable and efficient travel, the vehicle requires consideration and balancing of environmental fa…
Autonomous DrivingAutonomous VehiclesMotion PlanningLAMP: Lane-Aligned Motion Primitives for Feasible Trajectory Prediction
Motion forecasting is essential for autonomous driving systems to enable safe decision-making and planning in complex driving scenarios. While existing predictors excel at minimizing standard displacement errors, they of…
Trajectory PredictionAutonomous DrivingMotion ForecastingDRL-Based Trajectory Tracking for Motion-Related Modules in Autonomous Driving
Autonomous driving systems are always built on motion-related modules such as the planner and the controller. An accurate and robust trajectory tracking method is indispensable for these motion-related modules as a primi…
Autonomous DrivingDeep Reinforcement LearningRepresentation Learning