Path Planning for Autonomous Vehicles in Unknown Semi-structured Environments

By Dmitri A. Dolgov, Sebastian Thrun, Michael Montemerlo, James Diebel. 2010

946 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

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Citations in the corpus

A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “A* trajectories were optimized with numeric non-linear functions in [258].”
Path Planning for Autonomous Mobile Robots: A Review (2021, background): “Dolgov et al. [168] propose a version of A*, Hybrid A*, that prioritizes the feasibility of the resulting paths in exchange for the loss of optimality and completeness by rearranging the nodes after a path is found; in a way, the path is kinematically feasible.”
A Review of Motion Planning Techniques for Automated Vehicles (2016, methodology): “In path planning is often used to smooth previously computed trajectories as in [54] and also to compute trajectories from kinematic constraints [5].”
Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather (2019, background): “Multisensor Fusion Multisensor feeds in autonomous vehicles are typically fused to exploit varying cues in the measurements [43], simplify path-planning [15], to allow for redundancy in the presence of distortions [47], or solve for joint vision tasks, such as 3D object detection [64].”
Optimization-Based Collision Avoidance (2017, methodology): “do not review, or compare, optimization-based collision avoidance methods with alternative approaches such as those based on dynamic programming [14], reachability analysis [15, 16, 17], graph search [18, 19, 20], (random) sampling [21, 22, 23, 24], or interpolating curves [25]. Indeed, collision av”
Safe driving envelopes for path tracking in autonomous vehicles (2017, background): “This is especially true in normal driving scenarios characterized by low accelerations in which the kinematic vehicle model often used in path planners approximates vehicle behavior well [6][7].”
Autoware on Board: Enabling Autonomous Vehicles with Embedded Systems (2018, methodology): “and hybrid-state A* [21] are used to find the minimumcost path to the goal.”
A unified approach to local trajectory planning and control for autonomous driving along a reference path (2014, background): “The high-level planner is mainly responsible for finding a long-term, collision-free geometric path to reach the goal state by employing graph-based heuristic search (such as Hybrid A* [1], AD* [2]) or random sampling-based motion planning algorithms (such as RRTs [3]), etc.”
Research Advances and Challenges of Autonomous and Connected Ground Vehicles (2019, background): “Graph search algorithm searches for a minimum cost path on a graph, in which conﬁguration space is explicitly represented in a graph, like an occupancy grid or lattice [386], [387].”
Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives (2023, methodology): “Prevalent search-based methods include A* search [21] and dynamic programming (DP) [22].”
A Potential Field-Based Model Predictive Path-Planning Controller for Autonomous Road Vehicles (2017, background): “Path planning has been widely studied in robotics for obstacle avoidance [2]–[4].”
Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight (2019, methodology): “Our front-end kinodynamic path searching module is originated from the hybrid-state A* search ﬁrst proposed for autonomous vehicle [15].”
Navigation and planning in latent maps (2018, methodology): “We rely on hybridA∗ (Dolgov et al., 2010): we discretise the search space into a grid of N cells {cn}n∈[N ], which the conventional A∗search (Hart et al., 1968) can operate on.”
Self-Driving Cars: A Survey (2019, background): “[DOL10] propose the hybridstate A-star for the robotic car “Junior” [MON08] (Stanford”