# Pedestrian Prediction by Planning using Deep Neural Networks

arXiv: https://arxiv.org/abs/1706.05904 · HTML: https://mlsanity.com/paper/1706.05904/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2017.

## Built on by (most cited first, 11 of 11)

- [Learning to Predict Vehicle Trajectories with Model-based Planning](https://mlsanity.com/paper/2103.04027/) (2021)
- [Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Prediction](https://mlsanity.com/paper/2403.18447/) (2024)
- [Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approaches](https://mlsanity.com/paper/2111.06740/) (2021)
- [End-to-End Trajectory Distribution Prediction Based on Occupancy Grid Maps](https://mlsanity.com/paper/2203.16910/) (2022)
- [MSN: Multi-Style Network for Trajectory Prediction](https://mlsanity.com/paper/2107.00932/) (2021)
- [Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review](https://mlsanity.com/paper/2308.06419/) (2023)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [From Cognition to Precognition: A Future-Aware Framework for Social Navigation](https://mlsanity.com/paper/2409.13244/) (2024)
- [Occlusion-Aware Crowd Navigation Using People as Sensors](https://mlsanity.com/paper/2210.00552/) (2022)
- [Continuous Locomotive Crowd Behavior Generation](https://mlsanity.com/paper/2504.04756/) (2025)
- [Resonance: Learning to Predict Social-Aware Pedestrian Trajectories as Co-Vibrations](https://mlsanity.com/paper/2412.02447/) (2024)

## Abstract (arXiv)

Accurate traffic participant prediction is the prerequisite for collision avoidance of autonomous vehicles. In this work, we predict pedestrians by emulating their own motion planning. From online observations, we infer a mixture density function for possible destinations. We use this result as the goal states of a planning stage that performs motion prediction based on common behavior patterns. The entire system is modeled as one monolithic neural network and trained via inverse reinforcement learning. Experimental validation on real world data shows the system's ability to predict both, destinations and trajectories accurately.