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Currently, multiple countries around the world have begun deploying autonomous vehicles, including technologically advanced nations such as the United States, China, Germany, and Japan, where tests or commercial operations of L4-level autonomous driving have been conducted. Taking Malaysia as an example, although large-scale deployment on public roads has not yet been achieved, autonomous driving technology is developing rapidly. The government actively promotes it through frameworks like the *National Automotive Policy*. In 2025, 9D Intelligence and ALS jointly launched a public road trial operation of L4-level autonomous logistics vehicles, marking the transition of the technology from experimentation to practical application. Local enterprises, such as REKA with its CRETA accessories, and international brand collaboration projects have also accelerated the technology's implementation. It is expected that in the coming years, priority will be given to its adoption in closed scenarios such as logistics and ports. However, challenges such as defining legal responsibilities still need to be addressed, and full-scale deployment in the short term may remain limited.

Autonomous vehicles do utilize 5G networks in their technical implementation, but they are not entirely dependent on them. Currently, the development of autonomous driving technology in Malaysia is gradually integrating 5G communication technology. For example, in the 2020 Langkawi 5G demonstration project, driverless shuttle buses transmitted real-time data via 5G and collaborated with 3D-LiDAR sensors to achieve environmental perception and path planning. This low-latency, high-bandwidth communication capability can significantly improve the response speed of vehicles to complex road conditions, with particularly obvious advantages in scenarios such as multi-vehicle coordination or remote monitoring. However, it should be noted that autonomous driving systems adopt a multi-sensor redundancy design, and local perception devices such as millimeter-wave radars and visual cameras remain the core, while 5G primarily serves as a supplementary means for data interaction. The deployment of Tesla FSD in Malaysia demonstrates another technical approach: it achieves high-level autonomous driving based on a pure vision solution and onboard computing power, with low dependence on 5G. However, future integration of V2X (Vehicle-to-Everything) functionality would still require 5G support. Overall, 5G is an important enabler for improving the reliability of autonomous driving and expanding application scenarios, but at the current stage, technological implementation still requires balancing communication requirements with cost-effectiveness based on specific solutions.

Autonomous driving technology indeed relies on artificial intelligence (AI) to achieve its core functions, as fully demonstrated in the recently launched L4-level autonomous logistics vehicle project in Malaysia. The enterprise-level autonomous logistics vehicle jointly developed by 9D.ai and ALS processes sensor data, plans routes, and makes real-time decisions through AI algorithms, enabling it to autonomously complete logistics transportation tasks in complex urban and industrial environments. AI technology endows the vehicle with environmental perception, multi-object recognition, and dynamic obstacle avoidance capabilities. For instance, it can accurately identify obstacles and optimize driving paths in scenarios such as warehouses and ports, while continuously adapting to localized road conditions and climate characteristics through machine learning. This project not only validates the critical role of AI in autonomous driving but also enhances public trust in the technology through educational initiatives. In the future, with the iteration of AI models and regulatory improvements, autonomous driving will see further adoption in logistics, retail, and other sectors, driving the industry toward greater efficiency and intelligence.

China has achieved mass production and application of L3 conditional autonomous driving technology, and has launched pilot programs in cities such as Chongqing and Beijing. Vehicles equipped with special license plates can operate on designated road sections; for example, specific models of Changan Automobile and BAIC Arcfox have been approved for road use. The L3 system can take over driving in specific scenarios but requires the driver to assume control when requested by the system, marking a key leap from assisted driving to autonomous driving. Meanwhile, L4 driverless technology has been implemented in closed scenarios such as autonomous taxis and logistics vehicles. For instance, companies like Baidu Apollo Go have realized commercial operations in regions including the Middle East and Europe. At the policy level, China is promoting the transition of technology from test verification to large-scale application through regulatory improvements and pilot explorations, emphasizing safety bottom lines and responsibility definition—such as clarifying the division of responsibilities among car manufacturers, system suppliers, and drivers in the event of accidents. In the future, with algorithm optimization and infrastructure upgrades, autonomous driving will gradually expand from limited scenarios to open roads, but challenges such as safety redundancy in extreme scenarios and cost control need to be addressed. This process not only relies on technological progress but also requires the synchronous development of public trust and supporting social systems.

Currently, some automotive brands do adopt NVIDIA's chip technology, especially in the fields of intelligent driving and in-vehicle computing platforms. For example, General Motors has announced the expansion of its partnership with NVIDIA, stating that it will use NVIDIA's DRIVE AGX platform as the core hardware for its autonomous driving system. This platform can provide advanced driver assistance functions and support simulation for future smart factories. In the local market, the demand for NVIDIA chips in the data center industry also reflects its technological influence, although regulatory authorities are currently strengthening the tracking of the circulation of high-end chips. It is worth noting that the application of NVIDIA chips in the automotive sector mainly focuses on scenarios requiring high-performance computing, such as autonomous driving algorithm processing and in-vehicle infotainment systems. Such collaborations are usually realized through direct technical integration agreements between automakers and chip manufacturers. With the development of automotive intelligence, it is expected that more brands will consider adopting similar solutions to enhance the digital processing capabilities of their vehicles.