Cut-Out Scenario Generation With Reasonability Foreseeable Parameter Range From Real Highway Dataset for Autonomous Vehicle Assessment

Cut-Out Scenario Generation With Reasonability Foreseeable Parameter Range From Real Highway Dataset for Autonomous Vehicle Assessment

Blog Article

This study aims to generate test cases for scenario-based assessment of automated driving systems (ADS) when encounter a cut-out maneuver where the lead vehicle having changed lanes, revealing a new lead vehicle that, in some cases, is slower than the original lead (the cutting-out) vehicle.We extracted the cut-out scenarios from an established real-world traffic dataset recorded by instrumented vehicles on Japanese highways and Li-ion/Li-Po then defined them using vehicle kinematic parameters (velocities and distances).The extracted scenarios were analyzed based on the direct correlation between every two consecutive vehicles: a rear part that describes the correlation between the following vehicle and the cutting-out vehicle; and a frontal part that describes the correlation between the cutting-out vehicle and the preceding vehicle.Parameter ranges were quantified with a regression model and determined based on the risk acceptance threshold applied in the field of Japanese high-speed trains and annual exposure by professional highway drivers to produce a scenario space with a reasonably foreseeable range in which ADS may not produce crashes lest it performs worse than human drivers.A multi-dimensional distribution analytical approach was used to derive a correlation Jodhpur Clips between the following and preceding vehicles considering the initial longitudinal velocities.

Results suggest that when the time headway between the following vehicle and the cutting-out vehicle is equal to or more than 2 s, there should not have collision risks between the following vehicle and the preceding vehicle.These findings can help to understand normative driver behavior during cut-out scenarios and to generate accident-free scenario space for which ADS must perform flawlessly.