Motion planning in urban environments

By D. Ferguson, Thomas M. Howard, Maxim Likhachev. 2008

249 citations recorded by the source corpus.

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

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Citations in the corpus

A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning (2017, neutral): “Emails: { litingsun, chengpeng2014, wzhan, tomizuka} @berkeley.edu. i) graph-search-based, such as A ∗ (see [4]), ii) sampling-based (e.g., RRT, RRT*, see [5], [6]), iii) interpolating-curve-based [7], [8], and iv) optimization-based [9], [10].”
A Review of Motion Planning Techniques for Automated Vehicles (2016, methodology): “In [16] AD∗ was used by Boss, the winning vehicle of the DARPA Urban Challenge.”
Perception, Planning, Control, and Coordination for Autonomous Vehicles (2017, background): “In [233], the lane ahead was checked for presence of a vehicle traveling in the wrong direction on a collision path, where if triggered, a “defensive driving” maneuver would be executed to pull off the lane to the right side and stop.”
Optimization‐based autonomous racing of 1:43 scale RC cars (2015, methodology): “In [7] several approaches to this problem are presented, for example based on model predictive trajectory generation algorithms [31], modiﬁed A (cid:63) -algorithms [32] or RRTs [33].”
Motion planning for autonomous driving with a conformal spatiotemporal lattice (2011, methodology): “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.”
Self-Driving Cars: A Survey (2019, unknown): “Ferguson et al. (2008) uses a model-”
Local Path Planning for Off-Road Autonomous Driving With Avoidance of Static Obstacles (2012, background): “However, our approach in this paper can be extended to driving in a dynamic environment using the duality of the arc length and vehicle speed with dynamic collision checking [19], [30]–[33].”
Research Advances and Challenges of Autonomous and Connected Ground Vehicles (2019, methodology): “It was used in the CMU’s autonomous vehicle BOSS in Urban Challenge [395].”