# Pedestrian Emergence Estimation and Occlusion-Aware Risk Assessment for Urban Autonomous Driving

arXiv: https://arxiv.org/abs/2107.02326 · HTML: https://mlsanity.com/paper/2107.02326/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 6 Jul 2021.

## Builds on (most cited first, 7 of 7)

- [Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1610.03295/) (2016)
- [Self-Driving like a Human driver instead of a Robocar: Personalized comfortable driving experience for autonomous vehicles](https://mlsanity.com/paper/2001.03908/) (2020)
- [Occlusion-Aware Risk Assessment for Autonomous Driving in Urban Environments](https://mlsanity.com/paper/1809.04629/) (2018)
- [Tackling Occlusions & Limited Sensor Range with Set-based Safety Verification](https://mlsanity.com/paper/1807.01262/) (2018)
- [Limited Visibility and Uncertainty Aware Motion Planning for Automated Driving](https://mlsanity.com/paper/1810.13001/) (2018)
- [Safe Trajectory Synthesis for Autonomous Driving in Unforeseen Environments](https://mlsanity.com/paper/1705.00091/) (2017)
- [Pedestrian Collision Avoidance System for Scenarios with Occlusions](https://mlsanity.com/paper/1904.11566/) (2019)

## Built on by (most cited first, 4 of 4)

- [Using Collision Momentum in Deep Reinforcement Learning Based Adversarial Pedestrian Modeling](https://mlsanity.com/paper/2306.07525/) (2023)
- [Overcoming Blind Spots: Occlusion Considerations for Improved Autonomous Driving Safety](https://mlsanity.com/paper/2402.01507/) (2024)
- [From Shadows to Safety: Occlusion Tracking and Risk Mitigation for Urban Autonomous Driving](https://mlsanity.com/paper/2504.01408/) (2025)
- [Context-aware Attention-based Gaussian Mixture Models for Vehicular Trajectory Prediction](https://mlsanity.com/paper/2610.09174/) (2026)

## Abstract (arXiv)

Avoiding unseen or partially occluded vulnerable road users (VRUs) is a major challenge for fully autonomous driving in urban scenes. However, occlusion-aware risk assessment systems have not been widely studied. Here, we propose a pedestrian emergence estimation and occlusion-aware risk assessment system for urban autonomous driving. First, the proposed system utilizes available contextual information, such as visible cars and pedestrians, to estimate pedestrian emergence probabilities in occluded regions. These probabilities are then used in a risk assessment framework, and incorporated into a longitudinal motion controller. The proposed controller is tested against several baseline controllers that recapitulate some commonly observed driving styles. The simulated test scenarios include randomly placed parked cars and pedestrians, most of whom are occluded from the ego vehicle's view and emerges randomly. The proposed controller outperformed the baselines in terms of safety and comfort measures.