Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions

By Christos Katrakazas, Mohammed A. Quddus, Wen‐Hua Chen, Lipika Deka. 2015

755 citations recorded by the source corpus.

Abstract Currently autonomous or self-driving vehicles are at the heart of academia and industry research because of its multi-faceted advantages that includes improved safety, reduced congestion, lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a given origin to a given destination, motion planning methods incorporate searching for a path to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have been proposed in the literature. The purpose of this paper is to review existing approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1) finding a path, (2) searching for the safest manoeuvre and (3) determining the most feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments. Based on a critical review of existing methods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performance of planning algorithms at all three levels. Some promising areas of future focus have been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing technologies that are essential for planning with the aim of reducing the total cost of driverless vehicles. This critical review on planning techniques presented in this paper, along with the associated discussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research.

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

View Paper: https://www.semanticscholar.org/paper/99c3b2f50b3dfc86bf5feded6760df8b9e813584

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Citations in the corpus

Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review (2020, neutral): “…to adopt the optimal and safest feasible route for the AV to reach its destination, while considering all other possible obstacles on the road [7]. selected path, the control element Lastly, based on the selected path, the control element outputs the necessary values of acceleration, torque,…”
Optimization-Based Collision Avoidance (2017, methodology): “In the following, we review optimization-based approaches, and refer the interested reader to [26, 27, 28, 29, 30] for a comprehensive overview of the entire ﬁeld.”
A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving (2017, background): “Based on efforts from around the world, related technologies have been studied in depth [1,3].”
Research Advances and Challenges of Autonomous and Connected Ground Vehicles (2019, background): “To limit the scope of this survey to CAVs, only decision making and trajectory planning are addressed in detail in this Section, and a comprehensive discussion about route planning can be found in [53], [347].”
Planning and Decision-Making for Autonomous Vehicles (2018, background): “Two recent reviews (29, 30) provide a clear overview of the state of the art in motion planning for autonomous cars.”
Information-Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving (2017, methodology): “A key step in the design of an autonomous vehicle is the control methodology used to convert the vehicle state and world representation into physical actuation [1].”
Control of Connected and Automated Vehicles: State of the Art and Future Challenges (2018, background): “In the literature, path planning generally indicates the problem of planning future motion in the conﬁguration space of the vehicle, while trajectory planning indicates the search of a time-parametrized solution [59, 164].”
A human-like game theory-based controller for automatic lane changing (2018, background): “Obstacle prediction and risk assessment are usually considered part of the maneuver planning process (Katrakazas et al., 2015).”
The kinematic bicycle model: A consistent model for planning feasible trajectories for autonomous vehicles? (2017, background): “A full review on Motion Planning techniques can be found in [13] and [14].”