Deep Learning-based Vehicle Behaviour Prediction For Autonomous Driving Applications: A Review

TL;DR

This review paper surveys recent deep learning methods for vehicle behaviour prediction in autonomous driving, highlighting their advantages over traditional approaches in complex scenarios and discussing future research directions.

Contribution

It provides a comprehensive classification and analysis of state-of-the-art deep learning approaches for vehicle behaviour prediction, identifying research gaps and outlining future directions.

Findings

Deep learning approaches outperform traditional methods in complex environments.

Classification based on input representation, output type, and prediction method.

Identified key research gaps and potential future research directions.

Abstract

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 behaviour 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 superior 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 behaviour prediction in this paper. We firstly give an overview of the generic problem of vehicle behaviour 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 paper also discusses the performance of several well-known solutions, identifies the research gaps in the literature and outlines potential new research directions.