Why did the Robot Cross the Road? - Learning from Multi-Modal Sensor Data for Autonomous Road Crossing

TL;DR

This paper introduces a multi-modal sensor learning method for autonomous street crossing using laser and radar data, enabling robots to decide when it is safe to cross streets without traffic lights.

Contribution

It presents a novel approach relying solely on laser and radar data with a Random Forest classifier for safe street crossing decisions, validated through extensive real-world experiments.

Findings

Achieves high accuracy in predicting safe crossing moments

Demonstrates robustness across diverse street crossing scenarios

Outperforms alternative methods in safety and reliability

Abstract

We consider the problem of developing robots that navigate like pedestrians on sidewalks through city centers for performing various tasks including delivery and surveillance. One particular challenge for such robots is crossing streets without pedestrian traffic lights. To solve this task the robot has to decide based on its sensory input if the road is clear. In this work, we propose a novel multi-modal learning approach for the problem of autonomous street crossing. Our approach solely relies on laser and radar data and learns a classifier based on Random Forests to predict when it is safe to cross the road. We present extensive experimental evaluations using real-world data collected from multiple street crossing situations which demonstrate that our approach yields a safe and accurate street crossing behavior and generalizes well over different types of situations. A comparison to alternative methods demonstrates the advantages of our approach.