Dynamic road section risk identification model based on connected in-vehicle data

To dynamically identify road section risks, the research team designed an onboard unit to collect various dynamic driving behavior data when the Advanced Driver Assistance Systems (ADAS) are activated. To do this, we divided the roads into three categories (urban road, expressway, freeway) and established separate BN models to analyze the relationship between driving behavior and road section risk. These models were constructed based on natural driving data from 10,000 km, collected from vehicles equipped with ADAS. For road segment division, fixed-length intervals were used for freeways and urban expressways, while segments on urban roads were defined as the stretches between adjacent intersections. Using braking deceleration and time to collision, we identified near-crash events and classified them into high, medium, and low severity levels using the DBSCAN clustering algorithm. These near-crash events were then matched to the corresponding road section, assigning different weights based on their severity levels to evaluate the risk level of each segment. Additionally, driving behavior data, including velocity, lateral acceleration, longitudinal acceleration, yaw rate, accelerator position, steering angle, and steering angle velocity, were matched to the road segments. Finally, using the Netica software, Bayesian network models were constructed separately for urban roads, expressways, and freeways to identify driving risks at road segments. The models exhibited high sensitivity to observed nodes.