On August 21, local time, the New York City Department of Transportation granted Waymo, a subsidiary of Alphabet, a permit to test eight vehicles in Manhattan and Brooklyn, commencing at the end of September. This announcement swiftly captured the attention of the global technology and transportation sectors, with Silicon Valley and Wall Street alike buzzing with excitement. This comes on the heels of Waymo's landmark achievement in May, completing 10 million self-driving paid trips, marking another "exponential leap forward." As the first company to secure a permit for autonomous vehicle testing in New York, Waymo has ushered in a new era in the Robotaxi (autonomous taxi) competition.

However, the true focus for practitioners transcends being the "first permit" holder; it's the safety threshold it entails. New York stands as the city with the highest traffic density and most erratic human driving patterns in the world. Waymo's decision to bring its sensor array here is akin to publicly accepting the world's most arduous "safety college entrance exam."

From Phoenix to New York: Waymo's "Triple Jump" and the Robotaxi Landscape in the United States

Waymo undeniably stands as a pioneer and leader in the U.S. Robotaxi industry. As early as 2009, Waymo's predecessor, Google's autonomous driving project, embarked on a long journey of technological research, development, and road testing. After over a decade of refinement, Waymo has amassed profound technical expertise and vast operational data. Today, it operates a fleet of over 1,500 vehicles and has completed 10 million self-driving paid trips, with a presence in multiple cities including Austin, Los Angeles, San Francisco, and Phoenix. Recently, Waymo also announced plans for road tests in Philadelphia and a partnership with Uber to enter the Atlanta market, continuously expanding its footprint.

In terms of technology, Waymo adopts the mainstream approach of "multiple sensors + high-precision maps," with LiDAR serving as the core sensing component. Its fifth-generation autonomous driving system is equipped with 5 LiDARs, 8 cameras, and 4D millimeter-wave radars. Through multi-sensor fusion and coordination with high-precision maps, the vehicle achieves all-round, high-precision perception of the surroundings, offering a high degree of redundancy for safe driving. However, this technology comes at a high cost, with each vehicle costing approximately 7 times that of a Tesla, significantly hindering large-scale promotion.

Regarding business ecosystem construction, Waymo demonstrates a diversified layout. Its core business, Robotaxi, is operational in multiple U.S. cities with a charging standard of approximately $2.5 per mile (approximately RMB 11.1 per kilometer). As economies of scale emerge, future prices are expected to approach those of public transportation, further enhancing market competitiveness. Additionally, Waymo actively collaborates with automakers to provide autonomous driving technology solutions and license its technology, such as partnering with the Stellantis Group to modify the Pacifica model and reaching data-sharing agreements with automakers like Jaguar and Zeekr. In April this year, it even joined forces with Toyota to expand cooperation boundaries in the field of autonomous driving. Furthermore, Waymo has reached cooperation agreements with mobility platforms like Uber and Moove, integrating resources from all parties to expand service coverage and enhance brand influence.

However, Waymo faces stiff competition in the U.S. market. Tesla officially launched its autonomous taxi service in Austin, Texas, on June 22, 2025. Although currently limited in vehicle count and available only to invited customers, Tesla leverages its broad user base in the electric vehicle sector and continuous investment in intelligent driving algorithms. Unlike Waymo, Tesla adopts a pure vision-based approach, relying solely on cameras and AI technology. This reduces hardware costs but also poses challenges in complex environments where perception accuracy may be insufficient. Additionally, Amazon's Zoox is vigorously deploying Robotaxi. Its factory in Hayward, California, has commenced operations, planning to produce 10,000 Robotaxis annually by 2027, and will compete directly with Waymo in major U.S. markets such as Miami, Los Angeles, and Atlanta.

Domestic Robotaxi Development Status: Booming but Issues Emerge

Robotaxi development in China is also thriving, with multiple schools of thought vying for dominance. Pony.ai has launched fully driverless Robotaxi paid operation services in Beijing, Shanghai, Guangzhou, and Shenzhen, covering an area exceeding 2,000 square kilometers. In the second quarter of 2025, Pony.ai's total revenue increased by 75.9% year-on-year, with Robotaxi business revenue surging by 157.8% and passenger-side fare revenue growing by over 300%, marking significant progress in commercialization. WeRide also achieved notable results, with revenue of RMB 127 million in the second quarter of 2025, an increase of 60.8% year-on-year. Among this, Robotaxi business revenue was RMB 45.9 million, a substantial increase of 836.7% year-on-year, accounting for 36.1% of total revenue in the second quarter. WeRide also actively expanded its overseas market, signing cooperation agreements with Uber and the Dubai Roads and Transport Authority to deploy commercial Robotaxi services in Dubai, becoming the only technology company globally whose products hold autonomous driving licenses in six countries.

Furthermore, Baidu's "Luobo Kuaipao" has been operational in 10 cities including Wuhan, accumulating vast operational data and user feedback. After obtaining investment from GAC, Didi Autonomous Driving has accelerated its mass production process and is expected to achieve rapid technological iteration by leveraging its massive order data advantage in the mobility market. Hello Inc. also announced its entry into the Robotaxi sector in 2024, leveraging its user resources and operational experience in the shared mobility sector to carve out a share of this emerging market.

However, Robotaxi development in China faces numerous challenges. On the technical front, some enterprises have exposed shortcomings in actual operations. For instance, during the pilot project in Wuhan, "Luobo Kuaipao" vehicles experienced multiple instances of "immobility" due to their inability to accurately recognize objects like plastic bags and construction barriers, severely impacting the user experience. Socially, Robotaxi's low-price strategy has impacted the traditional online car-hailing and taxi industries, prompting protests from driver groups. Additionally, the popularization of autonomous driving technology may lead to massive driver unemployment, creating social employment pressure. In terms of policies and regulations, there are issues of multi-headed supervision and unclear powers and responsibilities. For example, multiple departments such as the Ministry of Transport, the Ministry of Industry and Information Technology, and the Ministry of Public Security cross-manage, but key issues like the qualification standards for remote safety officers remain unclarified. Local approval processes are cumbersome, requiring coordination among 12 departments and taking a long time. Furthermore, the "Road Traffic Safety Law" has yet to make clear provisions on liability division for L4 autonomous driving accidents, leaving enterprises facing significant legal risks and challenges in defining responsibilities.

The Importance of Autonomous Driving Safety: A Technological Imperative for Life Supremacy

Globally, approximately 1.3 million people die annually due to traffic accidents, with over 90% of these accidents caused by driver distractions, fatigue, or illegal operations. The birth of autonomous driving technology has a core mission: to enhance traffic safety and eliminate accidents caused by human factors at their source. Unlike common L2-level assisted driving, L4-level highly autonomous driving requires vehicles to handle various complex situations autonomously within specific scenarios without human intervention, truly shifting the safety logic from "human-machine co-driving" to "system-led," fundamentally avoiding collisions caused by human driver delays or errors.

The dilemmas of traditional single-vehicle intelligence primarily include:

Perception < 200 meters, with occlusion creating blind spots;

Decision-making < 50 ms, where computing power is the bottleneck;

Updates < monthly, with OTA being too slow.

AI Network: Transforming "Single-Vehicle Safety" into "System Safety"

In single-vehicle intelligent driving, vehicles primarily rely on onboard sensors (like cameras and radars) to perceive the surroundings, but this method has limitations. For instance, in blind spots such as intersections, curves, and ramps, the detection range and accuracy of onboard sensors are severely affected, making it difficult to perceive potential dangers in advance. Additionally, facing complex and ever-changing traffic scenarios like sudden accidents, irregular pedestrian behavior, and severe weather, single-vehicle intelligent algorithms often struggle to make quick and accurate decisions, leading to system misjudgments or slow reactions.

The emergence of the AI network provides new ideas and solutions to address these issues. Taking Mushroom Auto's MogoMind large model as an example, it possesses powerful real-time global perception capabilities, able to integrate roadside real-time data, break through the perception limitations of onboard sensors, accurately identify pedestrians, non-motorized vehicles, and other vehicles within vehicle blind spots, and promptly issue warnings to vehicles through the Internet of Vehicles technology, providing a basis for advance decision-making for drivers or autonomous driving systems. In severe weather conditions, MogoMind's large model visual enhancement technology can optimize and process images captured by onboard cameras, enhancing image clarity and recognizability, ensuring vehicles can still clearly perceive road conditions in complex environments such as low light, strong light interference, rain, and fog, thereby ensuring driving safety.

Furthermore, MogoMind offers real-time traffic condition analysis and information push capabilities, enabling real-time monitoring of road conditions ahead, quickly identifying information like accidents, construction, and traffic control, and promptly pushing these precise details to vehicles through the AI network. This helps drivers or autonomous driving systems plan optimal routes in advance and avoid congested or dangerous areas. In pedestrian behavior recognition and prediction, MogoMind can qualitatively analyze whether pedestrians have dangerous intentions such as crossing the road by analyzing factors like body posture, walking speed, and gaze direction, and provide reasonable driving behavior suggestions, effectively improving the reliability and safety of traffic decisions.

Waymo's obtainment of a permit to test autonomous vehicles in New York has undoubtedly injected new vitality into the global Robotaxi development and demonstrated a significant breakthrough in the commercialization of autonomous driving technology. However, whether it's the fierce competition in the U.S. market or the booming development in the domestic market, autonomous driving safety remains the lifeline of industry development. As Robotaxi moves from the experimental fields of Silicon Valley to the steel forests of Manhattan, there's only one rule:

"Prove that you are more afraid of death than humans before you are qualified to talk about business models."

In the future, with continuous technological advancements and improvements, Robotaxi is expected to truly realize the vision of safe, efficient, and convenient travel, reshaping the global urban transportation landscape. At the same time, those who prioritize safety as an infrastructure will own the "Manhattan moment" of Robotaxi.