Traditional road safety assessment methodologies do not recognize the driving environment’s fast-changing dynamics that influence the contextual complexity and, ultimately, its risk. This paper proposes a method to use diverse open-source sensor data (LiDAR) collected by Waymo autonomous vehicles to estimate the road environment’s complexity considering dynamic traffic conditions. The proposed contextual risk factor (CRF) model estimates the driving scene’s complexity using the density and proximity of the objects around the vehicle. The data was analyzed frame-by-frame, and contextual risk categories of high, medium, and low were assigned. The results revealed the objects in the scene well represent the contextual complexity. However, what the driver sees in front of them and within their forward reaction space is not directly representative of the complexity of the scene and vice versa.
Roadway Contextual Risk Assessment Using Dynamic Traffic Conditions Data Obtained from Autonomous Vehicles
ASCE International Conference on Computing in Civil Engineering 2021 ; 2022 ; Orlando, Florida
Computing in Civil Engineering 2021 ; 562-569
24.05.2022
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Simulating the Effect of Autonomous Vehicles on Roadway Mobility in a Microscopic Traffic Simulator
Springer Verlag | 2021
|ROADWAY INFORMATION DETECTION SENSOR DEVICE/SYSTEM FOR AUTONOMOUS VEHICLES
Europäisches Patentamt | 2018
|ROADWAY INFORMATION DETECTION SENSOR DEVICE/SYSTEM FOR AUTONOMOUS VEHICLES
Europäisches Patentamt | 2018
|Municipal Vehicles as Sensor Platforms to Monitor Roadway Traffic
Transportation Research Record | 2017
|