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

arXiv: https://arxiv.org/abs/1912.11676 · HTML: https://mlsanity.com/paper/1912.11676/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Dec 2019.

## Built on by (most cited first, 30 of 31)

- [Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys](https://mlsanity.com/paper/2303.17220/) (2023)
- [Deep Learning Methods for Vessel Trajectory Prediction based on Recurrent Neural Networks](https://mlsanity.com/paper/2101.02486/) (2021)
- [Improved Deep Reinforcement Learning with Expert Demonstrations for Urban Autonomous Driving](https://mlsanity.com/paper/2102.09243/) (2021)
- [Long-Tail Prediction Uncertainty Aware Trajectory Planning for Self-driving Vehicles](https://mlsanity.com/paper/2207.00788/) (2022)
- [Trajectory Prediction with Graph-based Dual-scale Context Fusion](https://mlsanity.com/paper/2111.01592/) (2021)
- [How Do We Fail? Stress Testing Perception in Autonomous Vehicles](https://mlsanity.com/paper/2203.14155/) (2022)
- [NetTraj: A Network-based Vehicle Trajectory Prediction Model with Directional Representation and Spatiotemporal Attention Mechanisms](https://mlsanity.com/paper/2106.11175/) (2021)
- [Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review](https://mlsanity.com/paper/2203.10939/) (2022)
- [Early Lane Change Prediction for Automated Driving Systems Using Multi-Task Attention-based Convolutional Neural Networks](https://mlsanity.com/paper/2109.10742/) (2021)
- [TIP: Task-Informed Motion Prediction for Intelligent Vehicles](https://mlsanity.com/paper/2110.08750/) (2021)
- [A Survey on Knowledge Graph-based Methods for Automated Driving](https://mlsanity.com/paper/2210.08119/) (2022)
- [Learning Interaction-aware Guidance Policies for Motion Planning in Dense Traffic Scenarios](https://mlsanity.com/paper/2107.04538/) (2021)
- [Relation-based Motion Prediction using Traffic Scene Graphs](https://mlsanity.com/paper/2212.02503/) (2022)
- [Evaluation of Differentially Constrained Motion Models for Graph-Based Trajectory Prediction](https://mlsanity.com/paper/2304.05116/) (2023)
- [Enhancing Fault Resilience of QNNs by Selective Neuron Splitting](https://mlsanity.com/paper/2306.09973/) (2023)
- [Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges](https://mlsanity.com/paper/2112.04698/) (2021)
- [Group Activity Recognition in Basketball Tracking Data -- Neural Embeddings in Team Sports (NETS)](https://mlsanity.com/paper/2209.00451/) (2022)
- [Benchmark for Models Predicting Human Behavior in Gap Acceptance Scenarios](https://mlsanity.com/paper/2211.05455/) (2022)
- [APPRAISER: DNN Fault Resilience Analysis Employing Approximation Errors](https://mlsanity.com/paper/2305.19733/) (2023)
- [Failure Detection for Motion Prediction of Autonomous Driving: An Uncertainty Perspective](https://mlsanity.com/paper/2301.04421/) (2023)
- [A Survey on Large Language Model-empowered Autonomous Driving](https://mlsanity.com/paper/2409.14165/) (2024)
- [Comparing merging behaviors observed in naturalistic data with behaviors generated by a machine learned model](https://mlsanity.com/paper/2104.10496/) (2021)
- [How Does Data Freshness Affect Real-time Supervised Learning?](https://mlsanity.com/paper/2208.06948/) (2022)
- [Allo-centric Occupancy Grid Prediction for Urban Traffic Scene Using Video Prediction Networks](https://mlsanity.com/paper/2301.04454/) (2023)
- [Spatiotemporal Capsule Neural Network for Vehicle Trajectory Prediction](https://mlsanity.com/paper/2303.02880/) (2023)
- [Affordance-based Reinforcement Learning for Urban Driving](https://mlsanity.com/paper/2101.05970/) (2021)
- [Vehicle trajectory prediction in top-view image sequences based on deep learning method](https://mlsanity.com/paper/2102.01749/) (2021)
- [Vehicle Type Specific Waypoint Generation](https://mlsanity.com/paper/2208.04987/) (2022)
- [A Novel Model for Driver Lane Change Prediction in Cooperative Adaptive Cruise Control Systems](https://mlsanity.com/paper/2305.01096/) (2023)
- [Learning and Predicting Multimodal Vehicle Action Distributions in a Unified Probabilistic Model Without Labels](https://mlsanity.com/paper/2212.07013/) (2022)

## Abstract (arXiv)

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.