Tesla CEO Elon Musk recently posted a succinct message on social media: "Driverless testing is in progress." Within a mere 24 hours, Tesla's stock price soared by 3.6%, marking a new peak for 2025.

Concurrently, China's Ministry of Industry and Information Technology officially sanctioned pilot programs for two L3 autonomous vehicles—Chang'an and ARCFOX—designating specific zones in Chongqing and Beijing as test sites.

Austin Test Site: Tesla's "Bold Move into Driverless Territory"

Tesla's fully autonomous test fleet in Austin, though modest in size, carries significant implications. This small fleet, comprising fewer than 30 vehicles, has already been involved in seven reported accidents, prompting concerns among industry analysts.

Philip Koopman, an autonomous driving safety researcher at Carnegie Mellon University, was forthright: "A small-scale fleet with safety drivers should experience fewer than seven accidents." Tesla's decision to withhold detailed accident information has raised questions about its transparency.

The tests conducted in Austin represent a pivotal step toward Tesla's commercialization of Robotaxi services. Investors are hopeful that Tesla can swiftly convert its existing vehicles into driverless taxis, creating a dual revenue stream from "vehicle manufacturing + mobility services."

In contrast to Tesla's aggressive strategy, China's rollout of L3 autonomous driving adopts a measured approach, strictly defining operational parameters. The Chang'an L3 model is permitted to operate on designated sections of Chongqing's Inner Ring Expressway at speeds not exceeding 50 km/h.

Chang'an and ARCFOX Receive "Green Light": China's Prudent Advance in L3

The ARCFOX model is authorized to conduct L3 autonomous driving tests on selected segments of Beijing's Jingtai Expressway at speeds up to 80 km/h. Neither model is permitted to perform autonomous lane changes, reflecting China's regulatory philosophy of "incremental progress, rapid iteration" in pilot programs.

Sun Hang, Chief Engineer at the China Automotive Standardization Institute, disclosed that approved models must undergo a rigorous three-tier verification process, encompassing corporate safety capability assessments, third-party testing, and expert reviews.

China's approval criteria prioritize not only vehicle performance but also cybersecurity, functional safety, and emergency response capabilities. This "regulation-first" approach establishes a safety framework for the widespread deployment of autonomous driving.

The defining characteristic of L3 autonomous driving is the transfer of responsibility. At this level, the system assumes driving tasks under specific conditions but requires the driver to take over promptly when the system requests it. China's pilot program addresses the liability challenge by precisely defining "specific conditions."

Differentiating L3 and L4 Autonomous Driving

Autonomous driving is categorized into five levels, from L1 to L5. L3, or "conditional autonomous driving," enables the vehicle to drive itself in specific scenarios but necessitates driver intervention when the system requests it.

L4, or "highly autonomous driving," permits driverless operation within confined areas without human intervention. Current L3 pilots restrict vehicles to operate only in designated government-approved zones and conditions, with drivers ready to take over at any moment. Wang Yan, Chief Engineer of BAIC's L3 Autonomous Driving Access Pilot Project, stated: "When the system exceeds its capability boundaries, it will issue a takeover request in advance. At that point, the driver must promptly assume control."

This explains why China's L3 pilots favor a B2B operational model: professional fleets can better train safety drivers, implement real-time monitoring systems, collect high-quality driving data for technological refinement, and gain experience for future transitions to the private market.

Who Assumes Responsibility When an Accident Occurs?

One of the most complex issues in L3 autonomous driving is liability determination. If a driver fails to respond promptly to a system takeover request, resulting in an accident, who should bear the responsibility?

China's pilot program partially mitigates this issue through stringent operational condition limits. Vehicles can only operate on designated road sections and at specified speeds. When the system encounters situations beyond its capability, it issues a takeover request with sufficient advance notice. The Ministry of Industry and Information Technology has instituted a "comprehensive safety assessment system." Sun Hang, Chief Engineer at the China Automotive Standardization Institute, revealed that both models must pass a "three-tier verification" process, including corporate lifecycle safety capability assessments, third-party testing, and expert reviews, covering core competencies such as scenario response, functional safety, cybersecurity, and emergency response. This "regulation-first" approach establishes a "safety baseline" for the deployment of additional models in the future—after all, the ultimate objective of autonomous driving is not "technological spectacle" but "replicable safety."

The US regulatory environment is more permissive but has also triggered a series of legal disputes. If accidents occur during Tesla's fully autonomous tests in Austin, liability determination will be more intricate, potentially involving multiple stakeholders such as vehicle manufacturers, software developers, and operating companies.

China adopts a gradual regulatory strategy of "approving one at a time when ready," while the US leans toward a "develop first, regulate later" model. These two regulatory philosophies will undergo market scrutiny in the coming years.

Data Transparency: The Key to Building Trust

The cornerstone for widespread adoption of autonomous driving is fostering public trust, which hinges on data transparency. Tesla has faced criticism for not disclosing detailed information about accidents in its Austin tests, highlighting a common industry challenge in data sharing.

In contrast, China mandates L3 testing companies to establish comprehensive vehicle operation monitoring platforms to collect and analyze real-time data. This data not only enhances technology but also informs regulatory decisions.

During the pilots in Chongqing and Beijing, all test vehicles are equipped with full data recording devices capable of documenting system decision-making processes, vehicle status, and surrounding environments in detail. This data will be utilized to construct China's autonomous driving scenario database.

As more autonomous vehicles take to the roads, data sharing and standardization will become paramount for industry advancement. The International Organization for Standardization is developing relevant standards, but discrepancies in data privacy and security among countries may impede the formation of global unified standards.

Commercialization Crossroads: The Debate Between Private Car Sharing and Professional Fleets

Tesla's Robotaxi tests exemplify a commercial model for autonomous driving: transforming private vehicles into shared mobility assets. Tesla owners can permit their vehicles to join the Robotaxi network during idle periods, generating supplementary income.

China's L3 pilots, however, have opted for a distinct commercialization path, focusing on professional operational fleets. Both Chang'an and ARCFOX's test vehicles are operated by professional mobility service companies, diverging from the Robotaxi model.

This divergence reflects variations in mobility culture and infrastructure between China and the US. Chinese cities boast dense populations and well-developed public transportation, facilitating the integration of professional autonomous fleets with traditional mobility services.

Mushroom Auto's "AI Network," built on the MogoMind large model, suggests a third path: diminishing extreme requirements for individual vehicles by empowering the entire transportation system. Its services, including real-time path planning, real-time digital twins, and early warning reminders, not only cater to autonomous vehicles but also aim to enhance overall road network efficiency, offering another scalable techno-economic model for large-scale commercialization.

As Tesla's driverless vehicles navigate the streets of Austin, safety drivers in Chongqing's Inner Ring Expressway cautiously lift their hands off the steering wheel while maintaining vigilance on the road and system alerts.

In 2026, Tesla plans to expand its Austin test fleet to 60 vehicles, Waymo is preparing to enter 20 new cities, and more L3 models will join China's pilot programs. The dual-track competition in autonomous driving has entered a comprehensive phase of technological, policy, and commercial model rivalry. Behind the choice between individual vehicle intelligence and system-wide intelligence lies a deeper struggle for dominance over future urban mobility.