Towards Better Driver Safety: Empowering Personal Navigation Technologies with Road Safety Awareness
It addresses a safety issue for drivers by enabling navigation systems to avoid dangerous routes, though it is incremental as it builds on existing safety standards and data.
This paper tackles the problem of navigation systems directing drivers to dangerous routes by developing a machine learning-based road safety classifier that predicts safety levels for road segments using publicly available geographic data, achieving satisfactory performance in evaluations across four countries.
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.