Scenario-based testing is an important part in intelligent vehicle (IV) development. The data volume of collected test scenarios is extremely large, and directly using all collected scenarios to test IVs will lead to extremely low testing efficiency. To solve this problem, a criticality assessment model (CAM) for IV test scenario based on interactive field feature (IFF) and hypergraph learning is proposed to quantify the test scenario criticality to improve the test efficiency. The IFF is constructed based on the potential field-based method to integrally consider the multidimensional coupling of scenario elements. In addition, the interaction between the fields generated by the ego vehicle and the driving environment is modeled based on Delaunay triangulation discretization method to accurately quantify the driving environment risk to the ego vehicle. The node and hyperedge of the hypergraph are used to model the individual dynamic evolution and group interaction characteristics of vehicles, respectively. Subsequently, a hypergraph learning network is constructed to extract features from the built hypergraph, IFF and traffic elements. Finally, the effectiveness, reasonableness and accuracy validation experiments are designed to validate the proposed CAM. Ablation experiment results show that the proposed IFF and hypergraph learning network enhance the CAM accuracy. The reasonableness validation results show that the proposed CAM can better find critical test scenarios than time-to-collision and time-head-way methods. The accuracy of the proposed CAM is compared through four real validation scenarios in the proving ground. The comparison results show that the proposed CAM can accurately output the quantified test scenario criticality.

To ensure that the camera functions (CFs) of intelligent vehicles can operate normally in rain and fog, sufficient tests for CFs are indispensable. In this work, the synthetic image generation model (SIGM) is proposed to generate images to synthesize rain and fog effects for testing CFs. First, to synthesize the rain blur effects on raw images, the geometric-physical fusion model is proposed based on the properties of raindrop microparticles and vehicle features. In this model, the position distribution of raindrops is determined by the geometric model part, and the image pixel effects are realized by the physical model part. Second, the atmospheric scattering model is used to synthesize the fog effect and contains two parts: an airlight estimation model to estimate airlight values and a transmittance estimation model, where the corrected image depth and fog microphysical information based on the Mie theory are used to determine the fog transmittance. Finally, SIGM is validated through real rain and fog in image quality, testing CFs with synthetic images and videos. The result absolute error sum obtained with SIGM (0.45) is smaller than that of the simulation software (0.57) and another synthetic method (0.65), and all unrecognized targets identified by SIGM match the real test results, whereas both the simulation software and another synthetic method fail to accurately test CF. The experimental results indicate that the proposed SIGM has higher authenticity and reliability than testing using the simulation platform and has higher efficiency, controllability, repeatability, and safety than testing using real vehicle.

Accident scenarios with long-tail characteristics are essential for advancing autonomous vehicles (AVs) functionality. Integrating such scenarios into the training process enhances adaptability to complex situations. However, the scarcity of accident data, due to their randomness and collection challenges, limits this integration. To address this issue, a framework named CRADLE is proposed, leveraging causal reinforcement learning (RL) and deep learning for accident scenario generation. Under extremely limited data conditions, CRADLE enables the construction of a highly diverse accident scenario database while ensuring consistency in accident causation. First, a scenario knowledge graph is constructed, incorporating both a scenario graph and an accident causation graph. Then, a scenario graph temporal prediction model trained on real driving data, generates a sampling space for scenario graphs. Subsequently, a causal inference module is developed to establish a mapping and transformation mechanism between temporal scenario graphs and accident causation graphs. Finally, a graph similarity measurement method is introduced to guide RL model in orderly sampling within the scenario graph sampling space, ensuring causally controlled scenario generation. The proposed method is applied to generate accident scenarios for two lane-changing situations: simultaneous lane changes and multi-lane changes. These scenarios are incorporated into the closed-loop self-evolution process of the autonomous driving algorithms. Experimental results demonstrate that the constructed accident scenario databases significantly improve the algorithm adaptability, reducing AV collision rates by approximately 76%.