Driving Safety Prediction and Safe Route Mapping Using In-vehicle and Roadside Data

TL;DR

This paper introduces a dynamic risk assessment model for road safety that integrates driver behavior, real-time traffic data, and historical crash information to generate visual risk heat maps for improved safety management.

Contribution

The study extends the Safe Route Mapping model by incorporating driver behavior analysis and real-time traffic data using machine learning and fuzzy logic for enhanced risk prediction.

Findings

The model accurately predicts conflict indices within 1-2 seconds.

Adding driver behavior features improves prediction performance.

Risk heat maps effectively visualize safety levels for authorities.

Abstract

Risk assessment of roadways is commonly practiced based on historical crash data. Information on driver behaviors and real-time traffic situations is sometimes missing. In this paper, the Safe Route Mapping (SRM) model, a methodology for developing dynamic risk heat maps of roadways, is extended to consider driver behaviors when making predictions. An Android App is designed to gather drivers' information and upload it to a server. On the server, facial recognition extracts drivers' data, such as facial landmarks, gaze directions, and emotions. The driver's drowsiness and distraction are detected, and driving performance is evaluated. Meanwhile, dynamic traffic information is captured by a roadside camera and uploaded to the same server. A longitudinal-scanline-based arterial traffic video analytics is applied to recognize vehicles from the video to build speed and trajectory profiles. Based on these data, a LightGBM model is introduced to predict conflict indices for drivers in the next one or two seconds. Then, multiple data sources, including historical crash counts and predicted traffic conflict indicators, are combined using a Fuzzy logic model to calculate risk scores for road segments. The proposed SRM model is illustrated using data collected from an actual traffic intersection and a driving simulation platform. The prediction results show that the model is accurate, and the added driver behavior features will improve the model's performance. Finally, risk heat maps are generated for visualization purposes. The authorities can use the dynamic heat map to designate safe corridors and dispatch law enforcement and drivers for early warning and trip planning.