Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 23, Issue 1, Pages 33-47Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2020.3012034
Keywords
TV; Autonomous vehicles; Sensors; Roads; History; Machine learning; Trajectory; Vehicle behaviour prediction; trajectory prediction; autonomous vehicles; intelligent vehicles; machine learning; deep learning
Categories
Funding
- Jaguar Land Rover
- U.K.-EPSRC [EP/N01300X/1]
- EPSRC [EP/N01300X/2] Funding Source: UKRI
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This article provides a comprehensive review of deep learning-based approaches for vehicle behavior prediction. It discusses the challenges and issues in behavior prediction and categorizes and reviews the most recent solutions based on input representation, output type, and prediction method. The article also evaluates the performance of several solutions and outlines potential future research directions.
Behaviour prediction function of an autonomous vehicle predicts the future states of the nearby vehicles based on the current and past observations of the surrounding environment. This helps enhance their awareness of the imminent hazards. However, conventional behavior prediction solutions are applicable in simple driving scenarios that require short prediction horizons. Most recently, deep learning-based approaches have become popular due to their promising performance in more complex environments compared to the conventional approaches. Motivated by this increased popularity, we provide a comprehensive review of the state-of-the-art of deep learning-based approaches for vehicle behavior prediction in this article. We firstly give an overview of the generic problem of vehicle behavior prediction and discuss its challenges, followed by classification and review of the most recent deep learning-based solutions based on three criteria: input representation, output type, and prediction method. The article also discusses the performance of several well-known solutions, identifies the research gaps in the literature and outlines potential new research directions.
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