Artificial Intelligence
Planning Algorithms
Autonomous driving in urban environments: Boss and the Urban Challenge
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part I
Junior: The Stanford entry in the Urban Challenge
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part II
Differentially constrained mobile robot motion planning in state lattices
Journal of Field Robotics - Special Issue on Space Robotics, Part I
Spatiotemporal state lattices for fast trajectory planning in dynamic on-road driving scenarios
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Parallel scheduling for cyber-physical systems: analysis and case study on a self-driving car
Proceedings of the ACM/IEEE 4th International Conference on Cyber-Physical Systems
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We present a motion planner for autonomous on-road driving, especially on highways. It adapts the idea of a on-road state lattice. A focused search is performed in the previously identified region in which the optimal trajectory is most likely to exist. The main contribution of this paper is a computationally efficient planner which handles dynamic environments generically. The Dynamic Programming algorithm is used to explore in spatiotemporal space and find a coarse trajectory solution first that encodes desirable maneuvers. Then a focused trajectory search is conducted using the "generate-and-test" approach, and the best trajectory is selected based on the smoothness of the trajectory. Analysis shows that our scheme provides a principled way to focus trajectory sampling, thus greatly reduces the search space. Simulation results show robust performance in several challenging scenarios.