Towards Better Driver Safety: Empowering Personal Navigation Technologies with Road Safety Awareness

TL;DR

This paper introduces a machine learning approach to incorporate road safety awareness into navigation systems, aiming to reduce accidents caused by unsafe route recommendations.

Contribution

It defines a standardized road safety concept for navigation systems and develops a classifier using publicly available geographic data to predict safety levels.

Findings

Classifier achieves satisfactory performance across four countries

Defines a practical safety standard for navigation integration

Discusses extension strategies for different regions

Abstract

Recent research has found that navigation systems usually assume that all roads are equally safe, directing drivers to dangerous routes, which led to catastrophic consequences. To address this problem, this paper aims to begin the process of adding road safety awareness to navigation systems. To do so, we first created a definition for road safety that navigation systems can easily understand by adapting well-established safety standards from transportation studies. Based on this road safety definition, we then developed a machine learning-based road safety classifier that predicts the safety level for road segments using a diverse feature set constructed only from large-scale publicly available geographic data. Evaluations in four different countries show that our road safety classifier achieves satisfactory performance. Finally, we discuss the factors to consider when extending our road safety classifier to other regions and potential new safety designs enabled by our road safety predictions.