Accidents have become a widespread societal problem on a global scale as the automotive industry has progressed. In contrast to the long-term safe driving environment, the likelihood of encountering safety-critical scenarios leading to traffic accidents while driving is minimal. However, these rare events are crucial for enhancing human or autonomous driving skills. Therefore, evaluating and refining the decision-making processes of human or autonomous vehicles requires scalable generation of long-tail traffic scenarios. These scenarios should be both realistic and challenging, but still partially solvable. In this paper, we propose an automated method for generating challenging scenarios. Our primary objective is to use a safety-critical scenario generation model based on a Conditional Variational Autoencoder (VAE) to increase the variety of scenarios by diversifying latent flows on the pre-trained trajectory representation model. The results show that our method can produce plausible scenarios, surpassing the baseline by over 10% in collision metric for scenario generation.
Diversifying Latent Flows for Safety-Critical Scenarios Generation
2024-09-24
850503 byte
Conference paper
Electronic Resource
English
British Library Conference Proceedings | 1998
|British Library Online Contents | 1998
|Suicidal Pedestrian: Generation of Safety-Critical Scenarios for Autonomous Vehicles
ArXiv | 2023
|Adversarial Generation for Autonomous Vehicles in Safety-Critical Ramp Merging Scenarios
Springer Verlag | 2025
|