# Socially Aware Crowd Navigation with Multimodal Pedestrian Trajectory Prediction for Autonomous Vehicles

arXiv: https://arxiv.org/abs/2011.11191 · HTML: https://mlsanity.com/paper/2011.11191/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2020.

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

- [Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph](https://mlsanity.com/paper/2203.01821/) (2022)
- [Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation](https://mlsanity.com/paper/2401.12275/) (2024)
- [Uncertainty-Aware DRL for Autonomous Vehicle Crowd Navigation in Shared Space](https://mlsanity.com/paper/2405.13969/) (2024)
- [EgoTraj-Bench: Towards Robust Trajectory Prediction Under Ego-view Noisy Observations](https://mlsanity.com/paper/2510.00405/) (2025)
- [ManiCast: Collaborative Manipulation with Cost-Aware Human Forecasting](https://mlsanity.com/paper/2310.13258/) (2023)
- [A Study on Learning Social Robot Navigation with Multimodal Perception](https://mlsanity.com/paper/2309.12568/) (2023)
- [Knowledge-aware Graph Transformer for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2401.04872/) (2024)
- [Stranger Danger! Identifying and Avoiding Unpredictable Pedestrians in RL-based Social Robot Navigation](https://mlsanity.com/paper/2407.06056/) (2024)
- [Pedestrian-Aware Motion Planning for Autonomous Driving in Complex Urban Scenarios](https://mlsanity.com/paper/2504.01409/) (2025)
- [Intention Enhanced Diffusion Model for Multimodal Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2508.04229/) (2025)
- [Occlusion-Aware Diffusion Model for Pedestrian Intention Prediction](https://mlsanity.com/paper/2511.00858/) (2025)
- [Enhancing Pedestrian Trajectory Prediction with Crowd Trip Information](https://mlsanity.com/paper/2409.15224/) (2024)
- [MUSCLE-NET: Predicted-Multiscale-Aware Network for Pedestrian Trajectory Forecasting](https://mlsanity.com/paper/2606.00471/) (2026)
- [Multi-Person Human Motion Forecasting in Complex Scenes](https://mlsanity.com/paper/2608.27039/) (2026)

## Abstract (arXiv)

Seamlessly operating an autonomous vehicle in a crowded pedestrian environment is a very challenging task. This is because human movement and interactions are very hard to predict in such environments. Recent work has demonstrated that reinforcement learning-based methods have the ability to learn to drive in crowds. However, these methods can have very poor performance due to inaccurate predictions of the pedestrians' future state as human motion prediction has a large variance. To overcome this problem, we propose a new method, SARL-SGAN-KCE, that combines a deep socially aware attentive value network with a human multimodal trajectory prediction model to help identify the optimal driving policy. We also introduce a novel technique to extend the discrete action space with minimal additional computational requirements. The kinematic constraints of the vehicle are also considered to ensure smooth and safe trajectories. We evaluate our method against the state of art methods for crowd navigation and provide an ablation study to show that our method is safer and closer to human behaviour.