# TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions

arXiv: https://arxiv.org/abs/1812.04767 · HTML: https://mlsanity.com/paper/1812.04767/

## Key facts

- Cited by 82 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2018.

## Built on by (most cited first, 30 of 82)

- [Scene Transformer: A unified architecture for predicting multiple agent trajectories](https://mlsanity.com/paper/2106.08417/) (2021)
- [On Adversarial Robustness of Trajectory Prediction for Autonomous Vehicles](https://mlsanity.com/paper/2201.05057/) (2022)
- [SocialVAE: Human Trajectory Prediction using Timewise Latents](https://mlsanity.com/paper/2203.08207/) (2022)
- [Video Prediction Recalling Long-term Motion Context via Memory Alignment Learning](https://mlsanity.com/paper/2104.00924/) (2021)
- [ROAD: The ROad event Awareness Dataset for Autonomous Driving](https://mlsanity.com/paper/2102.11585/) (2021)
- [A Dynamic Multi-Scale Voxel Flow Network for Video Prediction](https://mlsanity.com/paper/2303.09875/) (2023)
- [On Exposing the Challenging Long Tail in Future Prediction of Traffic Actors](https://mlsanity.com/paper/2103.12474/) (2021)
- [Human Trajectory Prediction via Counterfactual Analysis](https://mlsanity.com/paper/2107.14202/) (2021)
- [STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes](https://mlsanity.com/paper/2204.01026/) (2022)
- [CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoning](https://mlsanity.com/paper/2106.13364/) (2021)
- [PedFormer: Pedestrian Behavior Prediction via Cross-Modal Attention Modulation and Gated Multitask Learning](https://mlsanity.com/paper/2210.07886/) (2022)
- [Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directions](https://mlsanity.com/paper/2307.07527/) (2023)
- [S2TNet: Spatio-Temporal Transformer Networks for Trajectory Prediction in Autonomous Driving](https://mlsanity.com/paper/2206.10902/) (2022)
- [Variational Autoencoder-Based Vehicle Trajectory Prediction with an Interpretable Latent Space](https://mlsanity.com/paper/2103.13726/) (2021)
- [T4P: Test-Time Training of Trajectory Prediction via Masked Autoencoder and Actor-specific Token Memory](https://mlsanity.com/paper/2403.10052/) (2024)
- [LatentFormer: Multi-Agent Transformer-Based Interaction Modeling and Trajectory Prediction](https://mlsanity.com/paper/2203.01880/) (2022)
- [Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review](https://mlsanity.com/paper/2308.06419/) (2023)
- [Unlimited Neighborhood Interaction for Heterogeneous Trajectory Prediction](https://mlsanity.com/paper/2108.00238/) (2021)
- [Propagating State Uncertainty Through Trajectory Forecasting](https://mlsanity.com/paper/2110.03267/) (2021)
- [Egocentric Human Trajectory Forecasting with a Wearable Camera and Multi-Modal Fusion](https://mlsanity.com/paper/2111.00993/) (2021)
- [SCOUT: Socially-COnsistent and UndersTandable Graph Attention Network for Trajectory Prediction of Vehicles and VRUs](https://mlsanity.com/paper/2102.06361/) (2021)
- [Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers](https://mlsanity.com/paper/2106.12442/) (2021)
- [Another Vertical View: A Hierarchical Network for Heterogeneous Trajectory Prediction via Spectrums](https://mlsanity.com/paper/2304.05106/) (2023)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents](https://mlsanity.com/paper/2104.11980/) (2021)
- [METEOR:A Dense, Heterogeneous, and Unstructured Traffic Dataset With Rare Behaviors](https://mlsanity.com/paper/2109.07648/) (2021)
- [A Novel Benchmarking Paradigm and a Scale- and Motion-Aware Model for Egocentric Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2310.10424/) (2023)
- [RiskBench: A Scenario-based Benchmark for Risk Identification](https://mlsanity.com/paper/2312.01659/) (2023)
- [Heterogeneous Trajectory Forecasting via Risk and Scene Graph Learning](https://mlsanity.com/paper/2211.00848/) (2022)
- [D2-TPred: Discontinuous Dependency for Trajectory Prediction under Traffic Lights](https://mlsanity.com/paper/2207.10398/) (2022)

## Abstract (arXiv)

We present a new algorithm for predicting the near-term trajectories of road-agents in dense traffic videos. Our approach is designed for heterogeneous traffic, where the road-agents may correspond to buses, cars, scooters, bicycles, or pedestrians. We model the interactions between different road-agents using a novel LSTM-CNN hybrid network for trajectory prediction. In particular, we take into account heterogeneous interactions that implicitly accounts for the varying shapes, dynamics, and behaviors of different road agents. In addition, we model horizon-based interactions which are used to implicitly model the driving behavior of each road-agent. We evaluate the performance of our prediction algorithm, TraPHic, on the standard datasets and also introduce a new dense, heterogeneous traffic dataset corresponding to urban Asian videos and agent trajectories. We outperform state-of-the-art methods on dense traffic datasets by 30%.