In order to ensure the repeatability of simulation tests and reliability of test results, a simulation testing platform is built using Unity and Unreal Engine based on a systematic analysis of the definition and potential causes of performance consistency issues in autonomous driving simulation software, and static and dynamic scenarios are built at the same time. The study also involves the design of test cases, data processing scripts, automated testing scripts and the overall experimental process. Test results based on Unity and Unreal Engine simulation test platform are quantified through the inconsistency deviation. Influenced by various factors related to the simulation system’s software and hardware, experimental results reveal significant inconsistencies in multiple tests, with the maximum deviation reaching 15%. To address the identified deviation, this paper proposes several optimization strategies, including process optimization and power mode optimization, which are validated to reduce the deviation by more than 40%, thereby enhancing the reliability and consistency of simulation tests.

In order to facilitate a comprehensive understanding of the Autonomous Valet Parking (AVP) system including its behavioral limitations, and to ensure its Safety Of The Intended Function (SOTIF), the paper proposes the SOTIF analysis process for AVP systems. This procedure synthesizes the ISO 21448 and the System Theory Process Analysis (STPA) method, and elaborates on system safety and triggering conditions, layers triggering conditions and combines with fuzzy comprehensive evaluation to build SOTIF scenarios. The evaluation employs collision distance risk, collision time risk and braking deceleration risk as key performance indicators. The Criteria Importance Through Intercriteria Correlation (CRITIC) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods are utilized for index weighting and evaluation. Finally, SOTIF scores of AVP systems under different scenarios are obtained through real vehicle testing, clarifying unsafe scenarios and providing targeted guidance for product optimization.

To address the issue of limited path tracking ability of Ultra-thin Carrier Robot (UCR) in complex test scenarios in intelligent connected vehicle testing, this paper proposes an improved Stanley lateral motion control algorithm. Firstly, the kinematic model of the UCR is established based on the Ackermann steering principle, and the bicycle model is used to simplify the steering system of the UCR. Then, a fuzzy PID controller is introduced on the basis of the traditional Stanley algorithm in order to enhance the adaptive feedback capability of the algorithm. The results of the joint simulation of CarSim and MATLAB/Simulink show that the modified Stanley algorithm reduces the average lateral error by 50.67% and the maximum lateral error by 41.76% at different speeds compared with the traditional algorithm. The real-vehicle test further confirms that the improved algorithm performs well in medium and high-speed straight line and lane changing scenarios, with the average lateral error less than 0.05 m and the maximum lateral error less than 0.17 m. It meets the testing requirements of intelligent connected vehicles in different scenarios and realizes high-speed and high-precision lateral motion control of the UCR.

In order to enhance the testing and evaluation of systematicness and comprehensiveness of Moving Off Information System (MOIS) for commercial vehicles, the paper analyzes the evaluation requirements of commercial vehicle MOIS, clarifies the working principle of MOIS, and proposes specific testing and evaluation plans and improvement measures. Commercial vehicles with the same MOIS perception and recognition scheme but different sizes are used as test samples, the alarm triggering and ending timing, alarm duration, etc. of MOIS are evaluated and analyzed for different target objects, blind spot positions, and bias rates. The results show that the same scheme of MOIS installed on commercial vehicles of different sizes can meet regulatory requirements, and the monitoring effect of the system on the forward blind spot shows a trend of right side>left side>middle. Moreover, when the vehicle moves together with the longitudinal target object in front, the larger vehicle has better blind spot monitoring effect.

Three types of IoV communication network test platforms of the 5th Generation mobile communication technology (5G), Enhanced Ultra High Throughput (EUHT) and Long-Term Evolution-Vehicle to Everything (LTE-V2X) are constructed based on the closed test field. And four test scenarios are designed, including the full-field performance test, dynamic and static performance test, Vehicle-to-Infrastructure (V2I) communication performance test and Vehicle-to-Vehicle (V2V) communication performance test. Communication delay, data packet delivery rate and throughput are used as evaluation metrics to verify and comparatively analyze the performance of the above three communication networks in typical application scenarios, such as varying communication distances, different vehicle speeds, V2I communication uplink, downlink and hybrid transmission, and end-to-end V2V communication in real-vehicle tests. Test results indicate that the three above networks generally satisfy the requirements of IoV applications in dynamic traffic environment, among which 5G demonstrates the superior performance, followed by EUHT, but in terms of deployment cost and complexity, LTE-V2X has a significant advantage.

To enhance the safety and reliability of autonomous driving in mainline logistics, this paper summarizes the testing requirements for the mainline logistics autonomous driving system from three aspects including equipment operation, system functionality and overall performance testing. Additionally, a testing plan for the mainline logistics autonomous driving transportation system is designed, encompassing four key stages: preliminary testing, Software-in-the-Loop (SiL) testing, system basic functionality and performance testing and system operational efficiency testing and evaluation. The preliminary testing serves as the foundational task of the testing plan, verifying the basic operational conditions of the trunk logistics autonomous driving transportation system. Following the completion of preliminary testing, SIL testing will be conducted in specific scenarios. System testing will then realize the integrated system’s functionality and performance test in actual operational scenarios. Finally, the system operational efficiency testing and evaluation framework will guide the assessment of the system’s efficiency during real-world operations.