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.

Trajectory planning is crucial for ensuring the safety of intelligent vehicles (IVs) in autonomous driving, especially in complex traffic environments where perception and control uncertainties increase collision risks. Existing risk-aware planning methods often fail to capture the full scope of uncertainties through risk assessment and struggle to consistently integrate risk information throughout the trajectory generation process. Risk factors are often overshadowed by smoothness and dynamic feasibility considerations, leading to excessively high-risk trajectories that fail to meet human driving expectations for caution. To address these challenges, we propose a cognitive risk-aware hierarchical trajectory planning method that adaptively regulates driving caution based on cognitive risk. We introduce cognitive risk assessment using two fields: the anisotropic objective risk field, which accounts for the size and comprehensive motion uncertainty of surrounding obstacles, and the driving cognition field, which considers the IV motion trends and reflects its proactive cognition of objective risks. By fusing these fields, we calculate cognitive risk and incorporate it into a planning framework that combines trajectory search and optimization. By explicitly considering cognitive risk constraints in both stages, the method achieves complete cognitive risk awareness, generating adaptive, safe, and dynamically feasible smooth trajectories. Experimental results in various scenarios demonstrate the effectiveness and superiority of the proposed method. By incorporating cognitive risk assessment, the IV exhibits more cautious driving behavior, aligning with human expectations. Compared to three state-of-the-art methods, our method improves the minimum time-to-collision by over 20%, reduces lane-crossing time by more than 10%, and decreases the average yaw rate by over 12%. In summary, the proposed method ensures higher safety, improves lane-changing efficiency and smoothness.

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.

Driving risk prediction is important for the development of intelligent vehicles (IVs), and the rise of human-like driving requires a driving risk prediction system to match the subjective risk cognitive characteristics of human drivers. In this study, a subjective driving risk prediction model (SDRPM) for IVs is proposed and applied to lane-changing (LC) conditions. We regard the cognitive risk of the human driver as the coupling result of environmental objective risk and driver subjective cognition at the spatial and temporal scales. Then, an objective anisotropic risk field was built to describe risks in traffic environments, and a subjective spatiotemporal cognition field to describe the cognitive characteristics of human drivers. By combining the two fields, the spatiotemporal distribution features of the human driver’s cognitive risk were obtained and used as model inputs, and SDRPM output the predicted subjective driving risk level (SDRL) of the human driver. To quantify the SDRLs, six participants were recruited to watch 1,213 driving videos and report the SDRLs they cognized. Participants were provided with 360-degree driving video around the test vehicle and virtual reality glasses to ensure the reliability of the obtained SDRLs. Verification results showed that SDRPM has good predicted accuracy and long advance predicted time, with 97.53% predicted accuracy, which can reach 95.06% at 2 s before the LC point. Compared with six state-of-the-art models, SDRPM can improve the predicted accuracy while reducing the dimensions of input features. In summary, SDRPM can ensure the driving safety, and improve user acceptance and trust in IVs.

Interactive decision-making between multiple automated vehicles under unsigned intersections is a high-level dynamic decision-making scenario, greatly increasing the complexity of decision-making. In this situation, making the decision-making manner in accordance with the logic of human and guaranteeing driving safety is technically challenging. A multi-factor-enabled interactive decision-making method is proposed in this paper to realize such behavior, which employs multiple complementary factors and switchable modes in a dynamic game. More specifically, these factors are driving performance requirements, e.g., moving safety, smoothness comfort, fast passing, and surrounding space, as well as diversified driving styles suitable for different driver groups. Meanwhile, to improve the reasonability of automated driving and reduce the complexity of multi-vehicle games, switchable game modes are established to realize the dynamic adjustment mechanism. The effectiveness of the proposed method in resolving conflicts in a continuous interactive way is verified through extensive simulations. The results indicate the proposed method can reflect the interaction process between multi-agents, and improve compliance between intelligent decision-making and the logic of human.