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The core principle of autonomous driving technology is based on the collaborative operation of a closed-loop system consisting of perception, decision-making, and execution. The perception layer collects real-time environmental data through sensors such as lidar, millimeter-wave radar, and cameras. Lidar generates high-precision 3D point cloud maps, millimeter-wave radar excels at monitoring object speeds in adverse weather conditions, and cameras identify traffic signs and pedestrians. Multi-sensor fusion ensures comprehensive environmental perception. The decision-making layer analyzes data using deep learning algorithms, combines high-definition maps with real-time positioning (e.g., GPS + Inertial Measurement Unit), and completes path planning and behavioral decisions (such as lane changes or emergency obstacle avoidance) within 0.1 seconds. The execution layer precisely controls the throttle, brakes, and steering wheel through technologies like electric power steering and brake-by-wire systems, where response speed and accuracy directly impact safety. Currently, most mainstream vehicle models are equipped with L2-level systems (e.g., adaptive cruise control). L3-L4 systems are being gradually deployed in specific scenarios (such as highways or campuses), while fully autonomous driving (L5 level) still faces challenges like extreme weather adaptability and algorithmic ethics. In the future, with the widespread adoption of 5G communication and AI algorithm optimization, autonomous driving will further enhance reliability, but requires simultaneous improvements in supporting regulations and infrastructure.

The safety of autonomous vehicles is a complex issue that requires multi-dimensional evaluation. Currently, models equipped with L2-level intelligent driving assistance systems have made significant progress in the local market. For example, Proton e.MAS 7, with its Shield Short Blade Battery and ADAS system, has obtained a five-star certification in the ASEAN NCAP test, with its adult safety protection score nearly perfect. The all-new Proton SAGA has become the first model in ASEAN to pass the CMR motorcycle safety certification, and its functions such as AEB and LDW have undergone localized calibration, optimizing the false trigger rate for typical road conditions like irregularly shaped tunnels and mixed motorcycle traffic. However, Minister of Science, Technology and Innovation Chang Lih Kang clearly pointed out that the implementation of fully autonomous driving (L3 level and above) still faces challenges in regulations and liability attribution, making commercial operation difficult to achieve in the short term. Current technologies focus more on improving the reliability of ADAS through multi-sensor fusion and AI algorithms. For instance, Zhixing Technology has conducted tens of thousands of kilometers of actual tests to adapt to right-hand drive and local traffic habits, while the intelligent campus autonomous vehicle evaluation framework developed by UMPSA emphasizes the importance of physical testing and localized adaptation. Overall, existing autonomous driving technologies have demonstrated high safety in limited scenarios, but full-scale deployment still requires improvements in the legal framework and further technical validation. Consumers can prioritize models that have passed ASEAN NCAP certification and are equipped with mature ADAS functions.

LiDAR (Light Detection and Ranging) is a core sensor in autonomous driving systems. It accurately detects the distance, shape, and speed of target objects by emitting laser beams and measuring reflected signals. The working principle is based on Time of Flight (ToF) or Frequency-Modulated Continuous Wave (FMCW) technology. By calculating the time difference between the emission and reception of laser pulses and combining it with angle information, LiDAR generates high-precision 3D point cloud data to construct a digital model of the vehicle's surrounding environment in real time. Compared to cameras and millimeter-wave radars, LiDAR offers centimeter-level ranging accuracy, strong resistance to light interference (e.g., backlight or nighttime conditions), and effective identification of low-reflectivity objects (such as black vehicles). However, its performance is significantly affected by heavy fog or rain. Currently, mainstream automotive LiDAR systems operate at 905nm or 1550nm wavelengths, with 1550nm providing superior long-range detection and weather penetration capabilities, albeit at higher cost. Technologically, mechanical rotating designs are gradually being replaced by solid-state solutions (e.g., MEMS, optical phased arrays) to enhance reliability and reduce costs. In autonomous driving applications, LiDAR performs critical functions including environmental modeling, obstacle classification, and lane-level positioning, typically integrated with cameras and millimeter-wave radars in multi-sensor fusion systems to ensure perception redundancy. Modern 128-line high-resolution LiDAR systems can now produce detailed point cloud imagery, while chip-scale technologies are driving further miniaturization and mass-market adoption.

Currently, the prices of fully autonomous vehicles vary significantly. Entry-level models such as the Changan Benben E-Star start at approximately 25,000 Malaysian Ringgit after subsidies, while high-end models like the Tesla Model X can exceed 600,000 Malaysian Ringgit. Mainstream mid-range autonomous driving models such as the WM Motor W6 are priced between 95,000 and 130,000 Malaysian Ringgit, while the NIO ET7 starts at around 225,000 Malaysian Ringgit. The price differences are primarily driven by brand premium, sensor configuration (number of LiDAR units), computing platform tier (e.g., NVIDIA Orin chipset), and software algorithm maturity. Notably, Baidu's "Apollo Go" robotaxi service is planning market entry, with per-unit costs around 130,000 Malaysian Ringgit. The shared mobility model can substantially reduce individual usage costs. With progressing localization and widespread adoption of lithium iron phosphate battery technology, L4 autonomous vehicle prices are projected to gradually decline to the 150,000 Malaysian Ringgit mainstream range during 2026-2030, though fully driverless systems will remain concentrated in premium models above 300,000 Malaysian Ringgit in the near term.

Level 5 Autonomous Driving is the highest level of automotive automated driving, characterized by complete elimination of human intervention—vehicles can independently perform all driving operations under any road conditions, environments, and weather, including complex urban roads, unmarked rural sections, and extreme climate scenarios. Currently, no mass-produced passenger vehicles in the Malaysian market meet the L5 standard, but pilot applications of L4 autonomous logistics vehicles have emerged, such as the enterprise-level logistics vehicles co-developed by ALS and Zelos, which can achieve highly automated transportation in restricted areas while still requiring human supervision as redundant support. Although Tesla's FSD system provides functions like automatic lane changing on urban roads and signal recognition locally, it remains classified as L2+ level assisted driving and requires drivers to be ready to take over at any time. From a technical perspective, achieving L5 requires overcoming three major bottlenecks: first, deep integration of high-precision sensors and AI algorithms to handle Southeast Asia's dynamic traffic environment (such as mixed motorcycle traffic); second, the vehicle-grade computing platform must achieve over 1000 TOPS to meet real-time decision-making demands; third, establishing nationwide V2X vehicle-infrastructure coordination infrastructure. Brands like Xpeng Motors are positioning Malaysia as an ASEAN autonomous driving R&D hub, and their localized CKD production and AI technology deployment may provide a data foundation for future L5 implementation. Current L4 commercial vehicle pilots (such as the collaboration between 9Sight Intelligence and Pos Malaysia) have accumulated operational data for technical iteration, but the commercialization of L5 passenger vehicles still awaits regulatory refinement and clarification of insurance liability frameworks.