Motion planning for autonomous driving with a conformal spatiotemporal lattice

By Matthew McNaughton, C. Urmson, John M. Dolan, Jin-Woo Lee. IEEE International Conference on Robotics and Automation, 2011

376 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/71dc048c1cc564a8ae921f5e715e442f061d08af

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Citations in the corpus

Baidu Apollo EM Motion Planner (2018, methodology): “Direct methods (e.g., [4] and [5]) attempt to ﬁnd the optimal trajectory within SLT using either trajectory sampling or lattice search.”
Self-Driving Cars: A Survey (2019, background): “cceleration, and suitable for local planning and dynamic environments. However, they have high computational cost, because it evaluates every possible solution in the graph [GON16]. McNaughton et al. [MCN11] propose a conformal spatiotemporal state lattice for on-road trajectory planning. They const”
Integrating Deep Reinforcement Learning with Model-based Path Planners for Automated Driving (2020, background): “The pipe usually continues with scene understanding [12], assessment [13], planning [14] and finally ends with motor control.”
Trajectory planning for Bertha — A local, continuous method (2014, methodology): “Nevertheless, combinatorial methods have been applied to dynamic street scenarios [5], [6].”
Autoware on Board: Enabling Autonomous Vehicles with Embedded Systems (2018, methodology): “For this reason, Autoware preferably uses the spatiotemporal lattice-based algorithms to adapt motion plans to urban environments [25].”
A Review of Motion Planning for Highway Autonomous Driving (2020, methodology): “In [29], the authors use a cubic spiral for path generation, as do [80], and a cubic”
An Open Approach to Autonomous Vehicles (2015, methodology): “We therefore use conformal spatiotemporal lattices to adapt the motion plan to the environment.(10) State-of-the-art research encourages the implementation of these algorithms.”
A Review of Motion Planning Techniques for Automated Vehicles (2016, background): “time and speed dimensions as [39] and [50].”
A unified approach to local trajectory planning and control for autonomous driving along a reference path (2014, methodology): “Later, this motion planning technique is specified to be utilized on-road motion planning [11].”
What could move? Finding cars, pedestrians and bicyclists in 3D laser data (2012, background): “This is witnessed by a cornucopia of publications in this area [1], [2], [3].”
Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions (2015, background): “The problem of planning then reduces to a boundary value problem of connecting the original state with the required final state (McNaughton et al., 2011).”
The Architectural Implications of Autonomous Driving: Constraints and Acceleration (2018, background): “, downtown area in cities), it uses conformal lattices with spatial and temporal information to adapt the motion plan to the environment [45, 73].”
Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives (2023, methodology): “Conversely, non-PVD methods directly plan trajectories, which has the underlying merit to improve the solution optimality [22], [41], [42], [43].”