A Review of Motion Planning Techniques for Automated Vehicles

By David Sierra González, Joshué Pérez, Vicente Milanés Montero, Fawzi Nashashibi. 2016

1,465 citations recorded by the source corpus.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering

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Citations in the corpus

A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, background): “local planning review is presented in [19] where the taxonomy of motion planning was divided into four groups; graph-based planners, sampling-based planners, interpolating curve planners and numerical optimization approaches.”
A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning (2017, background): “Simultaneously satisfying the above requirements can be difficult and many planning and control approaches have been proposed [3].”
An End-to-End Deep Neural Network for Autonomous Driving Designed for Embedded Automotive Platforms (2019, methodology): “This recommends the J-Net model for deployment on low-performing embedded automotive platforms.”
Combining Optimal Control and Learning for Visual Navigation in Novel Environments (2019, background): “This is often an important consideration for real robots, where jerky trajectories might lead to compounding sensor errors, poor tracking, or even hardware damage [15].”
A Review of Motion Planning for Highway Autonomous Driving (2020, background): “The tactical part is usually divided into subparts, with at least one for behavioral planning and another for motion generation [18], [24], [27], [29].”
Path Planning for Autonomous Mobile Robots: A Review (2021, background): “[13] (2015) No Yes No No Yes Yes No Yes (NLP) Mac et al.”
Computing Systems for Autonomous Driving: State of the Art and Challenges (2020, background): “planner is developed [136], which can be divided into three”
VAD: Vectorized Scene Representation for Efficient Autonomous Driving (2023, background): “PETR [31] introduces 3D positional encoding to image features and uses detection queries to learn object features via attention [46] mechanism.”
A Motion Planning and Tracking Framework for Autonomous Vehicles Based on Artificial Potential Field Elaborated Resistance Network Approach (2020, background): “1 shows the general structure of the autonomous vehicle taking the four wheel drive electric vehicle as an example [8], [9], mainly consisting of the perception, data fusion, decision, and control layer [10].”
An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve (2021, neutral): “The purpose of path planning is to generate a collision-free route from the starting point to the destination, and the corresponding research has been ongoing ever since the inception and the implementation of mobile robots [1], [2].”
Graph Neural Networks for Intelligent Transportation Systems: A Survey (2023, methodology): “…the scene. owing to its importance, numerous studies have focused on this level of autonomous vehicles’ operation, and a wide range of tools and methods have been applied by researchers, from model predictive control (MPC) to deep learning-based algorithms, and end-to-end frameworks [138], [139].”
Model-free Deep Reinforcement Learning for Urban Autonomous Driving (2019, background): “Current autonomous driving decision making systems are focusing on the non-learning model-based approaches, which often requires to manually design a driving policy [12], [21].”
Research Advances and Challenges of Autonomous and Connected Ground Vehicles (2019, background): “And then, based on real-time vehicle states and the current environment (e.g. lane markings, vehicles, pedestrians, road signs, etc.) provided by the perception layer, the planning layer computes a local optimal trajectory through the decision making and trajectory planning online [51].”
Optimization-Based Collision Avoidance (2017, background): “We refer the interested reader to [26, 27, 28, 29, 30] for a comprehensive review on existing trajectory planning and obstacle avoidance algorithms.”