# Probabilistic Multi-modal Trajectory Prediction with Lane Attention for Autonomous Vehicles

arXiv: https://arxiv.org/abs/2007.02574 · HTML: https://mlsanity.com/paper/2007.02574/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2020.

## Built on by (most cited first, 28 of 28)

- [GOHOME: Graph-Oriented Heatmap Output for future Motion Estimation](https://mlsanity.com/paper/2109.01827/) (2021)
- [HOME: Heatmap Output for future Motion Estimation](https://mlsanity.com/paper/2105.10968/) (2021)
- [Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversals](https://mlsanity.com/paper/2106.15004/) (2021)
- [THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling](https://mlsanity.com/paper/2110.06607/) (2021)
- [LaPred: Lane-Aware Prediction of Multi-Modal Future Trajectories of Dynamic Agents](https://mlsanity.com/paper/2104.00249/) (2021)
- [LAformer: Trajectory Prediction for Autonomous Driving with Lane-Aware Scene Constraints](https://mlsanity.com/paper/2302.13933/) (2023)
- [The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review](https://mlsanity.com/paper/2308.05731/) (2023)
- [R-Pred: Two-Stage Motion Prediction Via Tube-Query Attention-Based Trajectory Refinement](https://mlsanity.com/paper/2211.08609/) (2022)
- [FloMo: Tractable Motion Prediction with Normalizing Flows](https://mlsanity.com/paper/2103.03614/) (2021)
- [Trajectory Prediction with Graph-based Dual-scale Context Fusion](https://mlsanity.com/paper/2111.01592/) (2021)
- [DICE: Diverse Diffusion Model with Scoring for Trajectory Prediction](https://mlsanity.com/paper/2310.14570/) (2023)
- [Hierarchical Latent Structure for Multi-Modal Vehicle Trajectory Forecasting](https://mlsanity.com/paper/2207.04624/) (2022)
- [Diverse Multiple Trajectory Prediction Using a Two-stage Prediction Network Trained with Lane Loss](https://mlsanity.com/paper/2206.08641/) (2022)
- [Context-Aware Timewise VAEs for Real-Time Vehicle Trajectory Prediction](https://mlsanity.com/paper/2302.10873/) (2023)
- [Intention-aware Denoising Diffusion Model for Trajectory Prediction](https://mlsanity.com/paper/2403.09190/) (2024)
- [Towards Explainable Motion Prediction using Heterogeneous Graph Representations](https://mlsanity.com/paper/2212.03806/) (2022)
- [Graph-Based Interaction-Aware Multimodal 2D Vehicle Trajectory Prediction using Diffusion Graph Convolutional Networks](https://mlsanity.com/paper/2309.01981/) (2023)
- [Context-Aware Scene Prediction Network (CASPNet)](https://mlsanity.com/paper/2201.06933/) (2022)
- [Taming the Long Tail of Deep Probabilistic Forecasting](https://mlsanity.com/paper/2202.13418/) (2022)
- [Towards trustworthy multi-modal motion prediction: Holistic evaluation and interpretability of outputs](https://mlsanity.com/paper/2210.16144/) (2022)
- [Heterogeneous Graph-based Trajectory Prediction using Local Map Context and Social Interactions](https://mlsanity.com/paper/2311.18553/) (2023)
- [A First Physical-World Trajectory Prediction Attack via LiDAR-induced Deceptions in Autonomous Driving](https://mlsanity.com/paper/2406.11707/) (2024)
- [Integrating spoken instructions into flight trajectory prediction to optimize automation in air traffic control](https://mlsanity.com/paper/2305.01661/) (2023)
- [A Fast and Map-Free Model for Trajectory Prediction in Traffics](https://mlsanity.com/paper/2307.09831/) (2023)
- [Exploring Driving Behavior for Autonomous Vehicles Based on Gramian Angular Field Vision Transformer](https://mlsanity.com/paper/2310.13906/) (2023)
- [Jointly Learning Agent and Lane Information for Multimodal Trajectory Prediction](https://mlsanity.com/paper/2111.13350/) (2021)
- [GC-GAT: Multimodal Vehicular Trajectory Prediction using Graph Goal Conditioning and Cross-context Attention](https://mlsanity.com/paper/2504.11150/) (2025)
- [PhysVarMix: Physics-Informed Variational Mixture Model for Multi-Modal Trajectory Prediction](https://mlsanity.com/paper/2507.19701/) (2025)

## Abstract (arXiv)

Trajectory prediction is crucial for autonomous vehicles. The planning system not only needs to know the current state of the surrounding objects but also their possible states in the future. As for vehicles, their trajectories are significantly influenced by the lane geometry and how to effectively use the lane information is of active interest. Most of the existing works use rasterized maps to explore road information, which does not distinguish different lanes. In this paper, we propose a novel instance-aware representation for lane representation. By integrating the lane features and trajectory features, a goal-oriented lane attention module is proposed to predict the future locations of the vehicle. We show that the proposed lane representation together with the lane attention module can be integrated into the widely used encoder-decoder framework to generate diverse predictions. Most importantly, each generated trajectory is associated with a probability to handle the uncertainty. Our method does not suffer from collapsing to one behavior modal and can cover diverse possibilities. Extensive experiments and ablation studies on the benchmark datasets corroborate the effectiveness of our proposed method. Notably, our proposed method ranks third place in the Argoverse motion forecasting competition at NeurIPS 2019.