Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives

Abstract

Intelligent vehicles (IVs) have gained worldwide attention due to their increased convenience, safety advantages, and potential commercial value. Despite predictions of commercial deployment by 2025, implementation remains limited to small-scale validation, with precise tracking controllers and motion planners being essential prerequisites for IVs. This paper reviews state-of-the-art motion planning methods for IVs, including pipeline planning and end-to-end planning methods. The study examines the selection, expansion, and optimization operations in a pipeline method, while it investigates training approaches and validation scenarios for driving tasks in end-to-end methods. Experimental platforms are reviewed to assist readers in choosing suitable training and validation strategies. A side-by-side comparison of the methods is provided to highlight their strengths and limitations, aiding system-level design choices. Current challenges and future perspectives are also discussed in this survey.

21 pages, 15 figures and 5 tables, in IEEE Transactions on Intelligent Vehicles

References in corpus (15)

• Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys
• A Survey of End-to-End Driving: Architectures and Training Methods
• A2D2: Audi Autonomous Driving Dataset
• Hierarchical Reinforcement Learning for Self-Driving Decision-Making without Reliance on Labeled Driving Data
• Learning a Driving Simulator
• Lyapunov-based Safe Policy Optimization for Continuous Control
• Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic
• Prioritized Experience-based Reinforcement Learning with Human Guidance for Autonomous Driving
• ROAD: The ROad event Awareness Dataset for Autonomous Driving
• Deep Q-Network Based Decision Making for Autonomous Driving
• Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Traffic
• SODA10M: A Large-Scale 2D Self/Semi-Supervised Object Detection Dataset for Autonomous Driving
• ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning
• Driving in Real Life with Inverse Reinforcement Learning
• Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration

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• Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys
• Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors
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• Autonomous Advanced Aerial Mobility -- An End-to-end Autonomy Framework for UAVs and Beyond
• Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challenges
• Social Force Embedded Mixed Graph Convolutional Network for Multi-class Trajectory Prediction
• Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions
• Toward parallel intelligence: an interdisciplinary solution for complex systems
• FRENETIX: A High-Performance and Modular Motion Planning Framework for Autonomous Driving
• HOPE: A Reinforcement Learning-based Hybrid Policy Path Planner for Diverse Parking Scenarios
• S4TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles
• Boosting Visual Recognition in Real-world Degradations via Unsupervised Feature Enhancement Module with Deep Channel Prior
• Spatiotemporal Receding Horizon Control with Proactive Interaction Towards Autonomous Driving in Dense Traffic
• A Reinforcement Learning-Boosted Motion Planning Framework: Comprehensive Generalization Performance in Autonomous Driving
• DeepIPC: Deeply Integrated Perception and Control for an Autonomous Vehicle in Real Environments
• Scenarios Engineering driven Autonomous Transportation in Open-Pit Mines
• Pedestrian-Aware Motion Planning for Autonomous Driving in Complex Urban Scenarios
• 3CSim: CARLA Corner Case Simulation for Control Assessment in Autonomous Driving
• SDR-GAIN: A High Real-Time Occluded Pedestrian Pose Completion Method for Autonomous Driving