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We present MBAPPE, a novel approach to motion planning for autonomous driving combining tree search with a partially-learned model of the environment.
“Congested traffic states in empirical observations and microscopic simulations”
Martin Treiber, Ansgar Hennecke and Dirk Helbing · 2000
Earlier work this paper cites.
“Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search”, 2006
Rémi Coulom · 2006
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“Bandit Based Monte-Carlo Planning”
Levente Kocsis and Csaba Szepesvári · 2006
Earlier work this paper cites.
“Stanley: The Robot That Won the DARPA Grand Challenge”
Sebastian Thrun et al · 2007
Earlier work this paper cites.
“A perception-driven autonomous urban vehicle”
John Leonard et al · 2008
Earlier work this paper cites.
“Odin: Team victortango’s entry in the darpa urban challenge”
Andrew Bacha et al · 2008
Earlier work this paper cites.
“End to end learning for self-driving cars”
Mariusz Bojarski et al · 2016
Earlier work this paper cites.
“Mastering the game of Go with deep neural networks and tree search”
David Silver et al · 2016
Earlier work this paper cites.
“Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm”
David Silver et al · 2017
Earlier work this paper cites.
“Pointnet: Deep learning on point sets for 3d classification and segmentation”
Charles Qi, Hao Su, Kaichun Mo and Leonidas Guibas · 2017
Earlier work this paper cites.
“Learning to drive in a day”
Alex Kendall et al · 2019
Earlier work this paper cites.
“End-to-end interpretable neural motion planner”
Wenyuan Zeng et al · 2019
Earlier work this paper cites.
“End-to-end interpretable neural motion planner”
Wenyuan Zeng et al · 2019
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“Combining planning and deep reinforcement learning in tactical decision making for autonomous driving”
Carl-Johan Hoel et al · 2019
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“Perceive, predict, and plan: Safe motion planning through interpretable semantic representations”
Abbas Sadat et al · 2020
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“Dsdnet: Deep structured self-driving network”
Wenyuan Zeng et al · 2020
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“End-to-end model-free reinforcement learning for urban driving using implicit affordances”
Marin Toromanoff, Emilie Wirbel and Fabien Moutarde · 2020
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“Mastering atari, go, chess and shogi by planning with a learned model”
Julian Schrittwieser et al · 2020
“Gohome: Graph-oriented heatmap output for future motion estimation”
Thomas Gilles et al · 2022
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“Transfuser: Imitation with transformer-based sensor fusion for autonomous driving”
Kashyap Chitta et al · 2022
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“Urban driver: Learning to drive from real-world demonstrations using policy gradients”
Oliver Scheel et al · 2022
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Zhiyu Huang, Haochen Liu and Chen Lv · 2023
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“PlanT: Explainable Planning Transformers via Object-Level Representations”
Katrin Renz et al · 2023
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“Parting with Misconceptions about Learning-based Vehicle Motion Planning”
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“Driving Maneuvers Prediction Based Autonomous Driving Control by Deep Monte Carlo Tree Search”
Jienan Chen et al · 2020
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“Autonomous driving at intersections: A critical-turning-point approach for left turns”
Keqi Shu et al · 2020
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“Vehicle Control with Prediction Model Based Monte-Carlo Tree Search”
Timothy Ha et al · 2020
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“Learning to drive from a world on rails”
Dian Chen, Vladlen Koltun and Philipp Krähenbühl · 2021
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“NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles”
K.. H. J · 2021
Cited alongside, same era.
“Lookout: Diverse multi-future prediction and planning for self-driving”
Alexander Cui et al · 2021
Cited alongside, same era.
Daniel Dauner, Marcel Hallgarten, Andreas Geiger and Kashyap Chitta · 2023
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“From Prediction to Planning With Goal Conditioned Lane Graph Traversals”
Marcel Hallgarten, Martin Stoll and Andreas Zell · 2023
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“GRI: General Reinforced Imitation and Its Application to Vision-Based Autonomous Driving”
Raphael Chekroun, Marin Toromanoff, Sascha Hornauer and Fabien Moutarde · 2023
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“Diffstack: A differentiable and modular control stack for autonomous vehicles”
Peter Karkus, Boris Ivanovic, Shie Mannor and Marco Pavone · 2023
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“LEADER: Learning Attention over Driving Behaviors for Planning under Uncertainty”
Mohamad Danesh, Panpan Cai and David Hsu · 2023
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“ADAPT: Efficient Multi-Agent Trajectory Prediction with Adaptation”
Görkay Aydemir, Adil Akan and Fatma Güney · 2023
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“End-to-end Autonomous Driving: Challenges and Frontiers”
Li Chen et al · 2023
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