# BiTraP: Bi-directional Pedestrian Trajectory Prediction with Multi-modal Goal Estimation

arXiv: https://arxiv.org/abs/2007.14558 · HTML: https://mlsanity.com/paper/2007.14558/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2020.

## Built on by (most cited first, 30 of 47)

- [SocialVAE: Human Trajectory Prediction using Timewise Latents](https://mlsanity.com/paper/2203.08207/) (2022)
- [Stepwise Goal-Driven Networks for Trajectory Prediction](https://mlsanity.com/paper/2103.14107/) (2021)
- [LOKI: Long Term and Key Intentions for Trajectory Prediction](https://mlsanity.com/paper/2108.08236/) (2021)
- [EigenTrajectory: Low-Rank Descriptors for Multi-Modal Trajectory Forecasting](https://mlsanity.com/paper/2307.09306/) (2023)
- [SingularTrajectory: Universal Trajectory Predictor Using Diffusion Model](https://mlsanity.com/paper/2403.18452/) (2024)
- [Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Prediction](https://mlsanity.com/paper/2403.18447/) (2024)
- [NOPA: Neurally-guided Online Probabilistic Assistance for Building Socially Intelligent Home Assistants](https://mlsanity.com/paper/2301.05223/) (2023)
- [PSI: A Benchmark for Human Interpretation and Response in Traffic Interactions](https://mlsanity.com/paper/2112.02604/) (2021)
- [Joint Metrics Matter: A Better Standard for Trajectory Forecasting](https://mlsanity.com/paper/2305.06292/) (2023)
- [Action-based Contrastive Learning for Trajectory Prediction](https://mlsanity.com/paper/2207.08664/) (2022)
- [Geometric Trajectory Diffusion Models](https://mlsanity.com/paper/2410.13027/) (2024)
- [Coupling Intent and Action for Pedestrian Crossing Behavior Prediction](https://mlsanity.com/paper/2105.04133/) (2021)
- [STGlow: A Flow-based Generative Framework with Dual Graphormer for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2211.11220/) (2022)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [Context-Aware Timewise VAEs for Real-Time Vehicle Trajectory Prediction](https://mlsanity.com/paper/2302.10873/) (2023)
- [A Novel Benchmarking Paradigm and a Scale- and Motion-Aware Model for Egocentric Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2310.10424/) (2023)
- [Recurrent Aligned Network for Generalized Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2403.05810/) (2024)
- [BiPOCO: Bi-Directional Trajectory Prediction with Pose Constraints for Pedestrian Anomaly Detection](https://mlsanity.com/paper/2207.02281/) (2022)
- [Continuous Locomotive Crowd Behavior Generation](https://mlsanity.com/paper/2504.04756/) (2025)
- [SocialMOIF: Multi-Order Intention Fusion for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2504.15616/) (2025)
- [Asymmetrical Bi-RNN for pedestrian trajectory encoding](https://mlsanity.com/paper/2106.04419/) (2021)
- [On-Board Pedestrian Trajectory Prediction Using Behavioral Features](https://mlsanity.com/paper/2210.11999/) (2022)
- [DESTINE: Dynamic Goal Queries with Temporal Transductive Alignment for Trajectory Prediction](https://mlsanity.com/paper/2310.07438/) (2023)
- [Context-aware Pedestrian Trajectory Prediction with Multimodal Transformer](https://mlsanity.com/paper/2307.03786/) (2023)
- [Sparse Pedestrian Character Learning for Trajectory Prediction](https://mlsanity.com/paper/2311.15512/) (2023)
- [Context-aware Multi-task Learning for Pedestrian Intent and Trajectory Prediction](https://mlsanity.com/paper/2407.17162/) (2024)
- [Mimir: Hierarchical Goal-Driven Diffusion with Uncertainty Propagation for End-to-End Autonomous Driving](https://mlsanity.com/paper/2512.07130/) (2025)
- [Social-DualCVAE: Multimodal Trajectory Forecasting Based on Social Interactions Pattern Aware and Dual Conditional Variational Auto-Encoder](https://mlsanity.com/paper/2202.03954/) (2022)
- [ASTRA: A Scene-aware TRAnsformer-based model for trajectory prediction](https://mlsanity.com/paper/2501.09878/) (2025)
- [SGNetPose+: Stepwise Goal-Driven Networks with Pose Information for Trajectory Prediction in Autonomous Driving](https://mlsanity.com/paper/2503.08016/) (2025)

## Abstract (arXiv)

Pedestrian trajectory prediction is an essential task in robotic applications such as autonomous driving and robot navigation. State-of-the-art trajectory predictors use a conditional variational autoencoder (CVAE) with recurrent neural networks (RNNs) to encode observed trajectories and decode multi-modal future trajectories. This process can suffer from accumulated errors over long prediction horizons (>=2 seconds). This paper presents BiTraP, a goal-conditioned bi-directional multi-modal trajectory prediction method based on the CVAE. BiTraP estimates the goal (end-point) of trajectories and introduces a novel bi-directional decoder to improve longer-term trajectory prediction accuracy. Extensive experiments show that BiTraP generalizes to both first-person view (FPV) and bird's-eye view (BEV) scenarios and outperforms state-of-the-art results by ~10-50%. We also show that different choices of non-parametric versus parametric target models in the CVAE directly influence the predicted multi-modal trajectory distributions. These results provide guidance on trajectory predictor design for robotic applications such as collision avoidance and navigation systems.