Safety Evaluation Using Counterfactual Simulations: The use of computational driver behavior models in crash avoidance systems and virtual simulations with optimal subsampling
The first objective of this thesis is to investigate how a driver model which includes road users’ comfortable behaviors in crash avoidance algorithms impacts the systems’ safety performance and the residual crash characteristics. Chinese car-to-two-wheeler crashes were targeted; Automated Emergency Braking (AEB) algorithms, which comprised the proposed crash avoidance systems, were compared to a traditional AEB algorithm. The proposed algorithms showed larger safety performance benefits. In addition, the similarities in residual crash characteristics regarding impact speed and location after different AEB implementations can potentially simplify the designs of in-crash protection system in future.
The second objective is to develop and apply a method for efficient subsampling in crash-causation-model-based scenario generation for virtual safety assessment. The method, which is machine-learning-assisted, actively and iteratively updates the sampling probability based on new simulation results. The crash-causation model is based on off-road glances and a distribution of driver maximum decelerations in critical situations. A simple time-to-collision-based AEB algorithm was used to demonstrate the assessment process as well as the benefits of combining crash-causation-model-based scenario generation and optimal subsampling. The sampling methods are designed to target specific safety benefit indicators, such as impact speed reduction and crash avoidance rate. The results of the study show that the proposed sampling method requires almost 50% fewer simulations than traditional importance sampling.
Future work aims to focus on applying the active sampling method to driver-model-based car-to-vulnerable road user (VRU) scenario generation. In addition to assessing conflict and crash avoidance system performance, a novel stopping criterion based on Bayesian future prediction will be further developed and demonstrated for use in experiments (e.g., as part of developing driver models) and virtual simulations (e.g., using driver-behavior-based crash-causation models). This criterion will be able to indicate when studies are unlikely to yield actionable results within the budget available, facilitating the decision to discontinue them while they are being run.
conflict and crash avoidance
Advanced Driver Assistance Systems
Automated Driving Systems
Chalmers, Mekanik och maritima vetenskaper, Fordonssäkerhet
Yang, X.; Lubbe, N.; Bärgman, J., 2022. Automated Emergency Braking algorithms based on comfort zone boundaries outperform traditional algorithm: Virtual benefit assessment for car-to-two-wheeler crashes in China. Under review.
Supporting the interaction of Humans and Automated vehicles: Preparing for the Environment of Tomorrow (Shape-IT)
Europeiska kommissionen (EU) (EC/H2020/860410), 2019-10-01 -- 2023-09-30.
Thesis for the degree of licentiate of engineering - Department of Applied Mechanics, Chalmers University of Technology: 2023:04
Thesis for the degree of Licentiate – Department of Mechanics and Maritime Sciences: 2023:04
Jupiter, Room Gamma
Opponent: Ulrich Sander, Volvo Cars
Shanghai United Road Traffic Safety Scientific Research Center pre-crash time-series data; Volvo rear-end crashes [dataset]