Deep Learning-Based Vehicle Behavior Prediction for Autonomous Driving Applications: A Review

• Published in: IEEE transactions on intelligent transportation systems Vol. 23; no. 1; pp. 33 - 47
• Main Authors: Mozaffari, Sajjad, Al-Jarrah, Omar Y., Dianati, Mehrdad, Jennings, Paul, Mouzakitis, Alexandros
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-01-2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Autonomous vehicles; Deep learning; History; intelligent vehicles; Machine learning; Roads; Sensors; State-of-the-art reviews; Trajectory; trajectory prediction; Vehicle behaviour prediction
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2020.3012034

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.