【新华财经调查】自动驾驶行业遭遇剧烈洗牌 车路云一体化面临“四道坎”

Core Insights - The automatic driving industry is facing a significant reshuffle, highlighted by the suspension of operations at the unicorn company Haomo Zhixing, which was once valued over $1 billion and seen as a leader in high-level autonomous driving in China [1] - Safety concerns are intensifying public trust issues in autonomous driving, with recent accidents involving autonomous vehicles in both China and the U.S. [1] - The current landscape shows nearly 500 domestic companies in the autonomous driving sector, indicating a seemingly prosperous market but underlying challenges due to capital withdrawal, technological bottlenecks, and safety anxieties [1] Technology Pathways - The debate over the technical routes for intelligent driving centers on how to safely advance towards full autonomy, with Tesla's pure vision approach contrasting with the multi-sensor fusion strategies of companies like Huawei and Momenta [2][3] - Tesla's advantage lies in its vast data collection from mass-produced vehicles, but it faces limitations in recognizing static objects and performing in low-visibility conditions [2] - Multi-sensor fusion solutions, while addressing some of Tesla's shortcomings, also present challenges such as complex data calibration between different devices [2] Levels of Automation - The "Automated Driving Classification" standard categorizes driving automation from L0 to L5, with L3 being a critical threshold for human intervention and system dominance [3] - Companies are cautious in their claims about automation levels, with some, like Huawei, using terms like "L2.9999" to describe their systems, while others boldly label their autonomous taxis as "L4" [3] Industry Challenges - The rapid expansion of low-speed autonomous vehicles in urban areas raises safety concerns, as these vehicles often violate traffic rules and create hazards [3] - The reliability of autonomous taxis is still dependent on multiple backup strategies and remote control, indicating that full operational capability is not yet achieved [3] Integration of Vehicle, Road, and Cloud - The industry is recognizing the need for a "vehicle-road-cloud" integration to address coordination gaps that lead to operational failures [5] - This integration aims to enhance safety and efficiency by providing advanced perception and decision-making capabilities beyond what individual vehicles can achieve [5][6] Pilot Projects and Efficiency Gains - Wuxi has emerged as a pilot city for vehicle-road-cloud integration, demonstrating significant improvements in traffic efficiency, with average traffic flow increasing by 15%-20% [6][7] - The cost-effectiveness of digital infrastructure is highlighted, as it requires only 1% of the investment compared to new road construction while achieving substantial efficiency gains [7] Future Challenges - Despite the potential of vehicle-road-cloud integration, challenges remain in data quality, investment returns, multi-party collaboration, and scalability across diverse urban environments [8][9] - The lack of unified data standards and governance can hinder the effective use of collected information, while the need for clear operational mechanisms and quantifiable benefits is critical for long-term success [8][9]