SYSTEMATIC EVALUATION AND INTEGRATION OF AI-DRIVEN ZELOS AUTONOMOUS DRIVING VEHICLES: ENHANCING SAFETY ON SIMULATION PLATFORMS

E-commerce, fueled by the digital revolution, has become a cornerstone of modern retail, driving demand for efficient last-mile logistics services. As online sales soar past $4 trillion, the need for streamlined, cost-effective delivery solutions is urgent, particularly in markets like China, where complex traffic conditions and high customer expectations complicate last-mile delivery. Autonomous driving technology offers a promising approach to meeting these challenges, enabling lower costs and improved delivery efficiency. However, cities such as Suzhou present unique obstacles for autonomous delivery vehicles (ADVs), with unpredictable traffic and diverse obstacles like pedestrians and bicycles. To tackle these issues, this research developed a high-capacity simulation platform capable of executing 300,000 scenarios weekly. It incorporates advanced routing algorithms, such as the Shortest Path Faster Algorithm (SPFA), and high-definition mapping (HDMap) for precise localization, supporting rigorous testing across varied urban logistics scenarios. The platform’s modular microservices architecture ensures scalability, enabling thorough validation of both software and hardware components in unmanned logistics vehicles. Findings demonstrate that the platform’s architecture, particularly its modular microservices and Protocol Buffers for data handling, optimizes the reliability and safety of autonomous systems in dense urban environments. Realistic scenario generation through SPFA routing and HDMap integration provides a robust environment for decision-making tests, contributing to enhanced operational stability and efficiency.

Practical Implications extend beyond autonomous driving, suggesting relevance to intelligent transportation systems, delivery drones, and smart cities. The platform’s high-throughput capacity underscores the importance of large-scale testing, enabling rapid development cycles with minimal dependence on real-world testing. This research provides a foundation for future improvements in simulation efficiency, scenario diversity, and applications across various sectors, paving the way for further advancements in autonomous technology.