Using Behavioral Cloning for Rear-End Crash Scenario Generation
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Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Modellbyggare
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Sammanfattning
Autonomous Driving (AD) technology has made significant breakthroughs in recent
years. Virtual safety assessment is a primary method for evaluating the safety of
the AD functions due to its efficiency, high experimental control, low risk, and costeffectiveness. However, the number of cases in existing crash datasets is insufficient
to provide an adequate evaluation of critical scenarios. Therefore, it is necessary
to generate synthetic crashes with high fidelity. Rear-end crashes, which has relatively low complexity (only involves the longitudinal movement of two vehicles) and
high frequency (the most common crash type), is suitable as the first type for crash
generation. Behavior Cloning (BC), which can reproduce vehicle behavior using a
simple neural network structure, has the potential to generate synthetic crash scenarios with high fidelity. This thesis proposes a crash generation framework that
combines a BC-based crash generation model with Hierarchical Clustering-based
post-processing to generate crashes with the same distribution as the given crash
dataset. In addition, as there is currently no established methodology for the assessment of generated cases, evaluation methods that combine both general statistical
metrics and domain knowledge are introduced and implemented. In general, the pro posed framework is capable of generating crash kinematics with high fidelity, as the
generated cases could pass both visual inspection and statistical evaluation. Future
work should focus on enhancing the robustness of the generation framework. The
generated synthetic crashes could be employed either for virtual safety assessments
of AD functions or rear-end crash kinematic analysis for research purposes.
Beskrivning
Ämne/nyckelord
Rear-end Crash, Scenario Generation, Virtual Safety Assessment, Behavior Cloning, Data Synthesis, Deep Learning