To address the safety validation challenges faced during the large-scale deployment and industrial application of Automated Driving System (ADS), and to bridge the gaps in existing methods, particularly regarding the systematic behavioral competency modeling, the semantic completeness of test scenarios, the scenario coverage of the Operational Design Domain (ODD), and the validation of boundary capabilities, a framework for behavioral competencies and scenario modeling of ADS is presented. Based on investigations of international technical regulations and automated driving pilot practices, the framework follows the main line of"ODD-behavioral competencies-test scenarios-validation criteria-risk assessment", adopts a functional, abstract, logical and concrete scenario representation, and integrates knowledge-driven, data-driven, and goal-oriented modeling mechanisms to achieve structured and traceable validation paths. For behavioral competency modeling, it covers 4 scenario types: nominal, critical, failure as well as ODD-boundary, and establishes mappings among competencies, scenarios, as well as validation criteria through object-event-response analysis. At the validation level, the formulation logic of performance metrics and validation criteria is clarified to support the assessment of validation coverage and residual risks. The framework can provide methodological references for ADS safety declarations and product compliance assessment.

To address safety challenges arising from dynamic variations in operational capability boundaries, shifts in driving responsibility, and the activation of risk mitigation strategies in automated driving systems under multiple operational modes, this paper proposes a safety argumentation method for operational mode transitions. The method constructs a three-layer finite state machine model of"operational mode-operational state-behavioral response"to formally describe multi-mode operation and transition logic. Furthermore, a three-dimensional verification model of"state machine path, scenario type, safety evidence chain" is proposed to systematically link regulatory safety requirements with engineering design, testing and validation activities, and safety evidence. Typical scenarios are used to demonstrate the complete process from safety requirement decomposition and model construction to validation implementation and safety argumentation, providing an engineering reference for the safety design, regulatory review, and compliance decision-making of multi-mode automated driving systems.

To address the challenge that the traditional static type approval model struggles to accommodate the full lifecycle safety governance of Automated Driving Systems (ADS), which are characterized by continuous software iteration, complex operational environments, and dynamic evolution of AI models, the paper constructs an In-Service Monitoring and Reporting (ISMR) mechanism and its implementation pathway based on institutional analysis and pilot practices. Employing normative analysis and mechanism design methods, a enterprise-side implementation mechanism covering operational data acquisition, data governance, safety analysis, monitoring reporting and safety improvement is proposed. Furthermore, a government-enterprise collaborative governance framework featuring "rule supply, operational execution, and evidence feedback" is established, alongside an event-triggered post-deployment safety assessment framework. The results demonstrate that the proposed mechanism, centered on operational evidence, effectively supports the continuous safety assurance of ADS. It enables the ongoing identification, analysis, and closed-loop mitigation of operational risks while providing a basis for the dynamic updating of safety cases.

To address the safety challenges posed by the global accelerated deployment of Driver Control Assistance Systems (DCAS), this study compares the regulatory paradigms of the European Union, China, and the United States, and reveals that UN R171 transforms general safety principles into quantifiable engineering constraints, thereby driving the evolution of DCAS from discrete feature sets into systematically controlled systems. The findings indicate that high-standard compliance drives firms to develop three core engineering capabilities: deterministic modeling of system boundaries, evidentiary governance across the lifecycle, and data-driven closed-loop optimization. These capabilities constitute the engineering foundation for migration toward Automated Driving Systems (ADS). Case studies reveal that enterprises differentiate into 2 strategic pathways: systematic integration and agile breakthroughs. The proposed regulation-driven capability-building model elucidates the transmission mechanism from regulatory constraints to strategic assets, providing a theoretical framework for transforming compliance costs into long-term competitive advantages. Accordingly, the study offers policy recommendations focusing on implementing capability-oriented compliance within enterprises and fostering a dynamically balanced regulatory governance system.

To reduce the low-frequency noise generated during the driving of electric vehicles and improve riding comfort, an active sound management system for electric vehicle cabins is designed. It is based on the FbLMS algorithm, which classifies the noise in the cockpit into useful noise and useless noise according to the length of occurrence and burstiness. The two types of noise are separated and processed through the audio of the vehicle and the error microphone arranged in the target noise reduction position. The results show that, by the active sound management system based on the mutation factor λ, the retention of useful mutational signals is achieved while the useless noise is suppressed, thus fulfilling the objective of active sound management in the intelligent cockpit.

To address the issue of poor test-to-test consistency when measuring the overall mechanical resistance of a vehicle using a chassis dynamometer as part of the wind tunnel method for determining road load, a controlled variable approach is adopted to investigate the influencing factors from 5 aspects: test temperature, tire pressure, warm-up speed, warm-up time, and loading mass. The overall mechanical resistance values are measured in actual vehicle tests under different test schemes. The results show that setting the test chamber temperature to 23 ℃, selecting a warm-up speed of 140 km/h with a warm-up time of 40 min, and setting the tire pressure and loading mass according to the standard requirements can effectively ensure test consistency and improve test efficiency.