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.

Longitudinal control systems in intelligent connected vehicle (ICV) significantly reduce driving workload; however, most existing designs overlook the driver’s psychological perception of car-following safety. This neglect can elevate physiological stress and erode trust in the system. To address this, this paper proposes a human-centered Connected Adaptive Cruise Control (C-ACC) strategy that integrates the driver’s individualized cognitive safety analysis into the control loop within a connected environment. Using naturalistic driving data that jointly capture traffic context, vehicle states, and driver physiology, we construct an individualized safety boundary in the speed–distance domain. The boundary is parameterized by a Sigmoid function fitted to physiological activation features and is incorporated as a soft constraint within a Model Predictive Control (MPC) framework. Crucially, the controller leverages Vehicle-to-Vehicle (V2V) communication to optimize tracking performance while proactively limiting states associated with elevated activation. The strategy was validated through driver-in-the-loop experiments using two real vehicles enabled with V2V communication. Experimental results demonstrate that, compared with baseline strategies, the proposed approach effectively reduces physiological activation and psychological tension while ensuring ride comfort and control stability, thereby enhancing the driver’s perceived safety and trust. This framework provides a principled path for personalized C-ACC design based on cognitive state estimation and is scalable to broader connected automated driving applications.

Long-tail scenarios are of great significance in improving the testing efficacy of autonomous vehicles, but they are difficult to collect due to their inherent randomness and high risk. The low-cost, high-value videos of vehicle-perspective accidents stored in tachographs provide a potentially important source for long-tail data collection. However, the lack of multisource synchronization information and the unknown, diverse camera parameters in tachograph videos make it difficult for the existing methods to accurately extract key information. In response to these issues, this work proposes a vehicle-perspective accident scenario restoration framework based on tachograph videos. First, using preprocessed accident videos as the input, a scenario semantic understanding module was constructed to help extract the pixel trajectories, conduct parameter calibration, and obtain pixel road boundaries. Then, robust detection and tracking algorithms were used to extract the pixel trajectories. Furthermore, a high-precision calibration algorithm was proposed based on the Geometrically Salient Feature Bundle Adjustment (GSFBA). Finally, the camera parameters were used to perform an inverse perspective transformation on the pixel coordinates, the actual road boundaries were extracted based on the mapping strength of the pixel road boundaries, and the trajectory information was optimized using a systematic trajectory processing module. The proposed method was verified at multiple levels based on both two simulation scenarios and two real-world scenarios. The results show that the proposed framework in this work can obtain the key information of the scenario with a high degree of restoration accuracy. It was effectively applied to the restoration process of accident scenarios.

The ultrasonic radar is a crucial part of the automatic parking system (APS) and requires thorough testing to ensure safety. The field operational test (FOT) is expensive and inefficient, making simulation testing the preferred method for evaluating autonomous driving systems. However, a gap remains between the performance of current simulation ultrasonic radar models and the real-world ultrasonic radars in real driving environments. To address these challenges, an ultrasonic radar in-the-loop accelerated test method for APS is proposed. First, a high-fidelity ultrasonic radar in-the-loop test bench is established and validated. To reduce the high cost and time duration of hardware-in-the-loop (HIL) testing, a Bayesian optimization (BO)-based accelerated testing framework is designed, minimizing test numbers by using the prior information to guide the search process. Finally, experiments are conducted using the ultrasonic radar in-the-loop test bench to compare the Bayesian accelerated test with the combination test (CT) for the automatic parking algorithm. The test results indicate that the ultrasonic radar in-the-loop test bench demonstrates greater authenticity and accuracy than the simulation software, while the HIL testing offers improved efficiency and safety compared to the FOT. Moreover, the efficiency of the Bayesian accelerated test is 4.9 times greater than that of the CT while still ensuring adequate scenario coverage.

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%.

Intelligent vehicle testing has been a hotspot in the field of intelligent vehicles. Due to the multi-dimensional parameters and the continuity of testing scenarios, a complete test of all scenarios requires a large amount of manpower and several material resources. In critical scenarios, deficiencies in an intelligent vehicle’s performance and defects of an algorithm can be exposed. Therefore, increasing the search efficiency and coverage of critical scenarios is key in improving scenario-based intelligent vehicle testing technology. In this study, a critical scenario search method for intelligent vehicle testing based on the social cognitive optimization (SCO) algorithm is proposed. This method has two main parts: global search and local search. The global search, based on the modified SCO algorithm, integrates the density peak clustering (DPC) algorithm and the cooling scheduling function, and aims to find all local aggregation areas of critical scenarios in a logical scenario space. The local search applies a multi-dimensional convolution algorithm to the global search results to find critical scenarios near the local aggregation areas. Finally, a longitudinal automatic driving algorithm is tested using the proposed method under a specified logical scenario in a simulation environment. The test results show that the proposed method can improve both the search efficiency and coverage of critical scenarios.

The scenario-based test method is the research hotspot of automated vehicle (AV) validation and verification (V&V), and testing with hazardous scenarios is of important means. An Optimization Searching (OS) method for enhanced generation in hazardous scenarios is proposed in this paper to efficiently explore functional boundary scenarios in a huge logical state space. The method is computationally tractable, and its generated experimental parameters are optimized using past test results. The method includes five essential modules. The Exploration and Exploitation module uses theMulti-arm banditmethod to obtain the greatest sum of the$TTC^{\mathbf {-1}}$(Time To Collision). The Parameter Moving Probability Determination module uses an analytic hierarchy process to ensure that influential parameters are more likely to move. The Step Size Determination module is built withLevy-stepto find a greater number of hazardous scenarios. The Memory Function module is used to avoid repeat experiments that can reduce computing efficiency. The Result Analysis module creates a hazard parameter space for subsequent tests. We tested an ACC (Adaptive Cruise Control) algorithm with a specified logical scenario in the virtual environment built by PreScan. The results showed that the OS method can effectively discover the dangerous range with the tested ACC algorithm, and its test speed can reach more than five times that of an exhaustive algorithm without prior knowledge.

Intelligent vehicle testing and validation technology is a hotspot in the field of intelligent vehicles. Millimeter-wave radar has been widely used in intelligent vehicles and advanced driving assistant systems (ADASs). Owing to low efficiency, high maintenance cost and limited scenario types, the traditional real-vehicle-testing can make it difficult to meet the requirements of intelligent vehicle testing. Also, the degree of reality of millimeter-wave radar sensor models in the virtual test platform is not convincing enough. To solve the mentioned problems, the millimeter-wave radar in-the-loop (mmRil) testing for intelligent vehicles is proposed in this study. First, the radar echo simulation method is developed. Next, based on the millimeter-wave flight time, Doppler frequency shift and the radar power attenuation equation, the mmRil test system is built. A geometric model of a radar target detection and a power attenuation model under bad weather conditions are established. Then, the accuracy of the testbench is verified. Finally, a typical intelligent vehicle control strategy is used for testing under various working conditions and bad weather using the mmRil testbench. The test results show that the mmRil testing has higher authenticity and accuracy than the simulation testing and can be used to test the intelligent vehicle algorithms under severe weather conditions. Compared with the real vehicle testing, the mmRil testing has higher efficiency, repeatability and safety. Therefore, the mmRil testing represents an indispensable testing method in the field of intelligent vehicle testing.