# Pedestrian Collision Avoidance System for Scenarios with Occlusions

## TL;DR

This paper presents a POMDP-based pedestrian collision avoidance system that enhances autonomous driving safety by reducing unnecessary braking in occluded scenarios, integrating with existing AEB systems.

## Contribution

It introduces a novel POMDP formulation for pedestrian detection under occlusions and proposes an integrated approach with AEB for improved safety and efficiency.

## Key findings

- Reduces unnecessary emergency braking in occlusion scenarios.
- Provides a robust collision avoidance policy under uncertainty.
- Offers a rigorous evaluation methodology for autonomous braking systems.

## Abstract

Safe autonomous driving in urban areas requires robust algorithms to avoid collisions with other traffic participants with limited perception ability. Current deployed approaches relying on Autonomous Emergency Braking (AEB) systems are often overly conservative. In this work, we formulate the problem as a partially observable Markov decision process (POMDP), to derive a policy robust to uncertainty in the pedestrian location. We investigate how to integrate such a policy with an AEB system that operates only when a collision is unavoidable. In addition, we propose a rigorous evaluation methodology on a set of well defined scenarios. We show that combining the two approaches provides a robust autonomous braking system that reduces unnecessary braking caused by using the AEB system on its own.