Predicting the Severity of Driving Scenario for Rear-End and Cut-In Collisions Using Potential Risk Indicator Extracted from Near-Miss Video Database
ISSN: 2327-5626, e-ISSN: 2327-5634
Published July 28, 2021 by SAE International in United States
Citation: Imaseki, T., Sugasawa, F., Kawamura, T., and Mouri, H., "Predicting the Severity of Driving Scenario for Rear-End and Cut-In Collisions Using Potential Risk Indicator Extracted from Near-Miss Video Database," SAE Int. J. Trans. Safety 9(2):2021.
The driving safety performance of autonomous driving vehicles must be ensured before on-road implementation. Because it is not realistic to evaluate every single test condition in real-world traffic, computer simulation methods can be used. The driving safety performance can be evaluated by simulating various driving scenarios and calculating surrogate indicators representing dangerous collision risk. This study used a near-miss database and introduced a surrogate indicator that represents a potential risk in the driving scenarios for rear-end and cut-in collisions. The near-miss video database includes several driving scenarios experienced by human drivers, such as dangerous situations that lead to accidents, potentially dangerous situations that have a risk probability to escalate into dangerous situations, and normal driving situations. A skilled and attentive human driver foresees dangerous situations while driving and avoids them. Therefore, autonomous driving vehicles, which should be safer than human driving, must avoid potentially dangerous situations, as well as overtly dangerous ones. Level 3 autonomous driving vehicles must be safely operated to prevent potentially dangerous situations for rear-end collisions and cut-in collisions, which are the most frequent danger cases on highways. A calculation method of surrogate indicators to predict the severity of driving scenarios for rear-end and cut-in collisions was developed. The near-miss video database was used to validate that these indicators can illustrate risk probabilities and help assess dangerous situations. Thus, dangerous situations and potentially dangerous situations in the driving scenarios for rear-end and cut-in collisions were quantified using the surrogate indicators, and the driving safety performance of autonomous driving vehicles could be evaluated.