# Multi-Vehicle Trajectory Prediction at Intersections using State and Intention Information

arXiv: https://arxiv.org/abs/2301.02561 · HTML: https://mlsanity.com/paper/2301.02561/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 75 references that are in the map.
- First posted on arXiv 6 Jan 2023.

## Builds on (most cited first, 20 of 20)

- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks](https://mlsanity.com/paper/1810.02244/) (2018)
- [On Evaluation of Embodied Navigation Agents](https://mlsanity.com/paper/1807.06757/) (2018)
- [VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation](https://mlsanity.com/paper/2005.04259/) (2020)
- [TrackFormer: Multi-Object Tracking with Transformers](https://mlsanity.com/paper/2101.02702/) (2021)
- [INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps](https://mlsanity.com/paper/1910.03088/) (2019)
- [The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections](https://mlsanity.com/paper/1911.07602/) (2019)
- [Multi-Agent Tensor Fusion for Contextual Trajectory Prediction](https://mlsanity.com/paper/1904.04776/) (2019)
- [EagerMOT: 3D Multi-Object Tracking via Sensor Fusion](https://mlsanity.com/paper/2104.14682/) (2021)
- [GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving](https://mlsanity.com/paper/1907.07792/) (2019)
- [On Offline Evaluation of Vision-based Driving Models](https://mlsanity.com/paper/1809.04843/) (2018)
- [Multi-Agent Connected Autonomous Driving using Deep Reinforcement Learning](https://mlsanity.com/paper/1911.04175/) (2019)
- [Top-view Trajectories: A Pedestrian Dataset of Vehicle-Crowd Interaction from Controlled Experiments and Crowded Campus](https://mlsanity.com/paper/1902.00487/) (2019)
- [Unlimited Neighborhood Interaction for Heterogeneous Trajectory Prediction](https://mlsanity.com/paper/2108.00238/) (2021)
- [Interaction-aware Multi-agent Tracking and Probabilistic Behavior Prediction via Adversarial Learning](https://mlsanity.com/paper/1904.02390/) (2019)
- [SMART: Simultaneous Multi-Agent Recurrent Trajectory Prediction](https://mlsanity.com/paper/2007.13078/) (2020)
- [A Reinforcement Learning Approach for Intelligent Traffic Signal Control at Urban Intersections](https://mlsanity.com/paper/1905.07698/) (2019)
- [Towards Generalizing Sensorimotor Control Across Weather Conditions](https://mlsanity.com/paper/1907.11025/) (2019)
- [LDNet: End-to-End Lane Marking Detection Approach Using a Dynamic Vision Sensor](https://mlsanity.com/paper/2009.08020/) (2020)

## Built on by (most cited first, 1 of 1)

- [INTENT: An LSTM Framework for Vehicle Intention Prediction in Intersection Scenarios with Comprehensive Ablation Analysis](https://mlsanity.com/paper/2607.08316/) (2026)

## Abstract (arXiv)

Traditional approaches to prediction of future trajectory of road agents rely on knowing information about their past trajectory. This work rather relies only on having knowledge of the current state and intended direction to make predictions for multiple vehicles at intersections. Furthermore, message passing of this information between the vehicles provides each one of them a more holistic overview of the environment allowing for a more informed prediction. This is done by training a neural network which takes the state and intent of the multiple vehicles to predict their future trajectory. Using the intention as an input allows our approach to be extended to additionally control the multiple vehicles to drive towards desired paths. Experimental results demonstrate the robustness of our approach both in terms of trajectory prediction and vehicle control at intersections. The complete training and evaluation code for this work is available here: urlhttps://github.com/Dekai21/MultiAgentIntersection.