# A Survey on Deep-Learning Approaches for Vehicle Trajectory Prediction in Autonomous Driving

arXiv: https://arxiv.org/abs/2110.10436 · HTML: https://mlsanity.com/paper/2110.10436/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2021.

## Built on by (most cited first, 21 of 21)

- [Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges](https://mlsanity.com/paper/2308.10407/) (2023)
- [Behavioral Intention Prediction in Driving Scenes: A Survey](https://mlsanity.com/paper/2211.00385/) (2022)
- [The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review](https://mlsanity.com/paper/2308.05731/) (2023)
- [Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directions](https://mlsanity.com/paper/2307.07527/) (2023)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [Uncertainty estimation for Cross-dataset performance in Trajectory prediction](https://mlsanity.com/paper/2205.07310/) (2022)
- [Multiclass-SGCN: Sparse Graph-based Trajectory Prediction with Agent Class Embedding](https://mlsanity.com/paper/2206.15275/) (2022)
- [C2F-TP: A Coarse-to-Fine Denoising Framework for Uncertainty-Aware Trajectory Prediction](https://mlsanity.com/paper/2412.13231/) (2024)
- [SEED4D: A Synthetic Ego--Exo Dynamic 4D Data Generator, Driving Dataset and Benchmark](https://mlsanity.com/paper/2412.00730/) (2024)
- [Multimodal Manoeuvre and Trajectory Prediction for Automated Driving on Highways Using Transformer Networks](https://mlsanity.com/paper/2303.16109/) (2023)
- [Reviving Life on the Edge: Joint Score-Based Graph Generation of Rich Edge Attributes](https://mlsanity.com/paper/2402.04046/) (2024)
- [ViTE: Virtual Graph Trajectory Expert Router for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2511.12214/) (2025)
- [How Does Traffic Environment Quantitatively Affect the Autonomous Driving Prediction?](https://mlsanity.com/paper/2301.04414/) (2023)
- [Informed Priors for Knowledge Integration in Trajectory Prediction](https://mlsanity.com/paper/2211.00348/) (2022)
- [DynFlowDrive: Flow-Based Dynamic World Modeling for Autonomous Driving](https://mlsanity.com/paper/2603.19675/) (2026)
- [ExAgt: Expert-guided Augmentation for Representation Learning of Traffic Scenarios](https://mlsanity.com/paper/2207.08609/) (2022)
- [Transfusor: Transformer Diffusor for Controllable Human-like Generation of Vehicle Lane Changing Trajectories](https://mlsanity.com/paper/2308.14943/) (2023)
- [From Observation to Prediction: LSTM for Vehicle Lane Change Forecasting on Highway On/Off-Ramps](https://mlsanity.com/paper/2601.14848/) (2026)
- [LDGNet: A Lightweight Difference Guiding Network for Remote Sensing Change Detection](https://mlsanity.com/paper/2504.05062/) (2025)
- [Lane-Frame Quantum Multimodal Driving Forecasts for the Trajectory of Autonomous Vehicles](https://mlsanity.com/paper/2511.17675/) (2025)
- [PhaseShift: Topology-Aware Data Harmonization and Model Consolidation Across Signalized Intersections](https://mlsanity.com/paper/2608.25275/) (2026)

## Abstract (arXiv)

With the rapid development of machine learning, autonomous driving has become a hot issue, making urgent demands for more intelligent perception and planning systems. Self-driving cars can avoid traffic crashes with precisely predicted future trajectories of surrounding vehicles. In this work, we review and categorize existing learning-based trajectory forecasting methods from perspectives of representation, modeling, and learning. Moreover, we make our implementation of Target-driveN Trajectory Prediction publicly available at https://github.com/Henry1iu/TNT-Trajectory-Predition, demonstrating its outstanding performance whereas its original codes are withheld. Enlightenment is expected for researchers seeking to improve trajectory prediction performance based on the achievement we have made.