# PORCA: Modeling and Planning for Autonomous Driving among Many Pedestrians

arXiv: https://arxiv.org/abs/1805.11833 · HTML: https://mlsanity.com/paper/1805.11833/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2018.

## Built on by (most cited first, 30 of 36)

- [BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Driving](https://mlsanity.com/paper/2205.09743/) (2022)
- [SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2104.01528/) (2021)
- [Partially Observable Markov Decision Processes in Robotics: A Survey](https://mlsanity.com/paper/2209.10342/) (2022)
- [Adaptive Trajectory Prediction via Transferable GNN](https://mlsanity.com/paper/2203.05046/) (2022)
- [Social-Transmotion: Promptable Human Trajectory Prediction](https://mlsanity.com/paper/2312.16168/) (2023)
- [FEND: A Future Enhanced Distribution-Aware Contrastive Learning Framework for Long-tail Trajectory Prediction](https://mlsanity.com/paper/2303.16574/) (2023)
- [SSAGCN: Social Soft Attention Graph Convolution Network for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2112.02459/) (2021)
- [Progressive Pretext Task Learning for Human Trajectory Prediction](https://mlsanity.com/paper/2407.11588/) (2024)
- [Language-Driven Active Learning for Diverse Open-Set 3D Object Detection](https://mlsanity.com/paper/2404.12856/) (2024)
- [Closing the Planning-Learning Loop with Application to Autonomous Driving](https://mlsanity.com/paper/2101.03834/) (2021)
- [The Role of Predictive Uncertainty and Diversity in Embodied AI and Robot Learning](https://mlsanity.com/paper/2405.03164/) (2024)
- [D2-TPred: Discontinuous Dependency for Trajectory Prediction under Traffic Lights](https://mlsanity.com/paper/2207.10398/) (2022)
- [MotionBEV: Attention-Aware Online LiDAR Moving Object Segmentation with Bird's Eye View based Appearance and Motion Features](https://mlsanity.com/paper/2305.07336/) (2023)
- [Aware of the History: Trajectory Forecasting with the Local Behavior Data](https://mlsanity.com/paper/2207.09646/) (2022)
- [Social Motion Prediction with Cognitive Hierarchies](https://mlsanity.com/paper/2311.04726/) (2023)
- [BP-SGCN: Behavioral Pseudo-Label Informed Sparse Graph Convolution Network for Pedestrian and Heterogeneous Trajectory Prediction](https://mlsanity.com/paper/2502.14676/) (2025)
- [ForceFormer: Exploring Social Force and Transformer for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2302.07583/) (2023)
- [Navigation In Urban Environments Amongst Pedestrians Using Multi-Objective Deep Reinforcement Learning](https://mlsanity.com/paper/2110.05205/) (2021)
- [Pedestrian-Robot Interactions on Autonomous Crowd Navigation: Reactive Control Methods and Evaluation Metrics](https://mlsanity.com/paper/2208.02121/) (2022)
- [Reinforcement Learning based Negotiation-aware Motion Planning of Autonomous Vehicles](https://mlsanity.com/paper/2107.03600/) (2021)
- [Sliding Sequential CVAE with Time Variant Socially-aware Rethinking for Trajectory Prediction](https://mlsanity.com/paper/2110.15016/) (2021)
- [Intention-Aware Navigation in Crowds with Extended-Space POMDP Planning](https://mlsanity.com/paper/2206.10028/) (2022)
- [JRDB-Traj: A Dataset and Benchmark for Trajectory Forecasting in Crowds](https://mlsanity.com/paper/2311.02736/) (2023)
- [Multi-Camera Asynchronous Ball Localization and Trajectory Prediction with Factor Graphs and Human Poses](https://mlsanity.com/paper/2401.17185/) (2024)
- [ViTE: Virtual Graph Trajectory Expert Router for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2511.12214/) (2025)
- [IA-LSTM: Interaction-Aware LSTM for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2311.15193/) (2023)
- [Learning responsibility allocations for multi-agent interactions: A differentiable optimization approach with control barrier functions](https://mlsanity.com/paper/2410.07409/) (2024)
- [SG-LSTM: Social Group LSTM for Robot Navigation Through Dense Crowds](https://mlsanity.com/paper/2303.04320/) (2023)
- [Pedestrian-Aware Motion Planning for Autonomous Driving in Complex Urban Scenarios](https://mlsanity.com/paper/2504.01409/) (2025)
- [TPAD: Identifying Effective Trajectory Predictions Under the Guidance of Trajectory Anomaly Detection Model](https://mlsanity.com/paper/2201.02941/) (2022)

## Abstract (arXiv)

This paper presents a planning system for autonomous driving among many pedestrians. A key ingredient of our approach is PORCA, a pedestrian motion prediction model that accounts for both a pedestrian's global navigation intention and local interactions with the vehicle and other pedestrians. Unfortunately, the autonomous vehicle does not know the pedestrian's intention a priori and requires a planning algorithm that hedges against the uncertainty in pedestrian intentions. Our planning system combines a POMDP algorithm with the pedestrian motion model and runs in near real time. Experiments show that it enables a robot vehicle to drive safely, efficiently, and smoothly among a crowd with a density of nearly one person per square meter.