# Maneuver-based Trajectory Prediction for Self-driving Cars Using Spatio-temporal Convolutional Networks

arXiv: https://arxiv.org/abs/2109.07365 · HTML: https://mlsanity.com/paper/2109.07365/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2021.

## Built on by (most cited first, 14 of 14)

- [Long-Tail Prediction Uncertainty Aware Trajectory Planning for Self-driving Vehicles](https://mlsanity.com/paper/2207.00788/) (2022)
- [Cognitive Accident Prediction in Driving Scenes: A Multimodality Benchmark](https://mlsanity.com/paper/2212.09381/) (2022)
- [Heterogeneous Trajectory Forecasting via Risk and Scene Graph Learning](https://mlsanity.com/paper/2211.00848/) (2022)
- [Multimodal Manoeuvre and Trajectory Prediction for Automated Driving on Highways Using Transformer Networks](https://mlsanity.com/paper/2303.16109/) (2023)
- [DiPA: Probabilistic Multi-Modal Interactive Prediction for Autonomous Driving](https://mlsanity.com/paper/2210.06106/) (2022)
- [KI-GAN: Knowledge-Informed Generative Adversarial Networks for Enhanced Multi-Vehicle Trajectory Forecasting at Signalized Intersections](https://mlsanity.com/paper/2404.11181/) (2024)
- [MEAT: Maneuver Extraction from Agent Trajectories](https://mlsanity.com/paper/2206.05158/) (2022)
- [Beyond RMSE: Do machine-learned models of road user interaction produce human-like behavior?](https://mlsanity.com/paper/2206.11110/) (2022)
- [GRANP: A Graph Recurrent Attentive Neural Process Model for Vehicle Trajectory Prediction](https://mlsanity.com/paper/2404.08004/) (2024)
- [SwapTransformer: highway overtaking tactical planner model via imitation learning on OSHA dataset](https://mlsanity.com/paper/2401.01425/) (2024)
- [Deterministic World Models for Closed-loop Reachability Analysis of End-to-End Vision-based Control](https://mlsanity.com/paper/2512.08991/) (2025)
- [MonoPIC -- A Monocular Low-Latency Pedestrian Intention Classification Framework for IoT Edges Using ID3 Modelled Decision Trees](https://mlsanity.com/paper/2304.00206/) (2023)
- [Predictive Maneuver Planning with Deep Reinforcement Learning (PMP-DRL) for comfortable and safe autonomous driving](https://mlsanity.com/paper/2306.09055/) (2023)
- [Multi-agent Traffic Prediction via Denoised Endpoint Distribution](https://mlsanity.com/paper/2405.07041/) (2024)

## Abstract (arXiv)

The ability to predict the future movements of other vehicles is a subconscious and effortless skill for humans and key to safe autonomous driving. Therefore, trajectory prediction for autonomous cars has gained a lot of attention in recent years. It is, however, still a hard task to achieve human-level performance. Interdependencies between vehicle behaviors and the multimodal nature of future intentions in a dynamic and complex driving environment render trajectory prediction a challenging problem. In this work, we propose a new, data-driven approach for predicting the motion of vehicles in a road environment. The model allows for inferring future intentions from the past interaction among vehicles in highway driving scenarios. Using our neighborhood-based data representation, the proposed system jointly exploits correlations in the spatial and temporal domain using convolutional neural networks. Our system considers multiple possible maneuver intentions and their corresponding motion and predicts the trajectory for five seconds into the future. We implemented our approach and evaluated it on two highway datasets taken in different countries and are able to achieve a competitive prediction performance.