Planning Long Dynamically Feasible Maneuvers for Autonomous Vehicles

By Maxim Likhachev, D. Ferguson. Int. J. Robotics Res., 2008

619 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Automotive Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/1f8ca38a1fa455db3388c617697cc91300c59bc6

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Citations in the corpus

Sampling-based algorithms for optimal motion planning (2011, background): “These have also been successfully demonstrated on various robotic platforms (Dolgov et al. 2009; Likhachev and Ferguson 2009).”
Motion planning for autonomous driving with a conformal spatiotemporal lattice (2011, background): “We compute paths between vertices in our conformed lattice using the iterative numerical method of [6], also used by the Tartan Racing team[1][4] in the 2007 DARPA Urban Challenge.”
Path Planning for Autonomous Vehicles in Unknown Semi-structured Environments (2010, methodology): “However, this free-space planner often produces trajectories that are not well suited to semi-structured environ- ments, such as real parking lots.”
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].”
SIPP: Safe interval path planning for dynamic environments (2011, background): “Therefore, for practical purposes, a common approach to producing faster plans is to treat the environment as though it were static [5], [9].”
Survey of Robot 3D Path Planning Algorithms (2016, methodology): “Authors in [54] introduced a method to reuse information from previous explorations and update information through the affected and relevant portions of the exploring space.”
Search-based planning for manipulation with motion primitives (2010, methodology): “Finally, treating a planning problem as finding a good quality path in a graph is advantageous because it allows one to incorporate complex cost functions, complex constraints and represent easily arbitrarily shaped obstacles with grid-like data structures [4], [11].”
A Review of Motion Planning Techniques for Automated Vehicles (2016, background): “It is suitable for searching spaces mostly known a priori by the vehicle [30], but costly in terms of memory and speed for vast areas.”
Incremental Sampling-based Algorithms for Optimal Motion Planning (2010, background): “In particular, they were extended to run in an anytime fashion [48], [49], deal with dynamic environments [49], [50], and handle systems with differential constraints [51].”
Optimization-Based Collision Avoidance (2017, methodology): “In this article, we do not review, or compare, optimization-based collision avoidance methods with alternative approaches, such as those based on dynamic programming [17], reachability analysis [18]–[20], graph search methods, such as A and Hybrid A [21]–[23], (random) sampling, such as rapidly expl”
Opportunities and challenges with autonomous micro aerial vehicles (2012, background): “, 2005) and it may be possible to use these primitives with lattice-based motion planning techniques which have been shown to work for simpler kinematic primitives in non-holonomic vehicles (Likhachev and Ferguson, 2009).”
Path Planning for Autonomous Mobile Robots: A Review (2021, methodology): “The latter consist of making the edges based upon motion primitives, so the resulting path is ensured to be feasible given the robot mobility constraints, especially when using Graph Search algorithms, as in the work of Likhachev and Ferguson [27], Bergman et al. [28].”
A Motion Planning and Tracking Framework for Autonomous Vehicles Based on Artificial Potential Field Elaborated Resistance Network Approach (2020, methodology): “In grid-based approaches, an algorithm is proposed in [14] generating maneuvers for high-speed autonomous vehicles over large distances, which is based on Anytime Dynamic A∗.”
Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions (2015, background): “Likhachev and Ferguson (2009) use a multi-resolution state lattice with high resolution near the vehicle and the goal and low resolution elsewhere.”
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.”