Scaling RoboTaxis: Why High-Performance Digital Solid-State LiDAR Is Non-Negotiable

The development of LiDAR and robotaxis is deeply intertwined. Their progress together serves as a prime example of how technological breakthroughs and commercial adoption drive each other in the autonomous driving sector. LiDAR perceives the environment by emitting laser beams, enabling high-precision acquisition of distance and contour information of surrounding objects to construct reliable three-dimensional environmental maps.This capability has established it as a core sensor in the perception systems of Robotaxis. 

In L4 and above autonomous driving systems, LiDAR stands out with its high precision and reliability. It complements cameras, millimeter-wave radars and other sensors to form a comprehensive perception system that enables vehicles to make safe decisions in complex traffic scenarios. Especially in the safety-critical application of Robotaxi, the precise three-dimensional environmental data provided by LiDAR is a key enabler for achieving true driverless operation. 

In recent years, with the gradual introduction of autonomous driving regulations worldwide, the Robotaxi industry is entering an accelerated phase of commercialization. This article traces the technological evolution and application history of LiDAR in the Robotaxi sector. It reveals how these two fields have mutually driven each other's growth and analyzes the future trends toward large-scale commercialization. 

Phase 1: Technological Emergence and Early Exploration (2004–2015)

The evolution of both LiDAR and Robotaxi has progressed from technical validation through pilots to commercial scaling. 

The application of LiDAR in autonomous driving initially emerged from academic research and competitive challenges: 

•The Catalytic Effect of the DARPA Challenges: The three autonomous vehicle challenges organized by DARPA (Defense Advanced Research Projects Agency) between 2004 and 2007 served as a key technological catalyst. In the second event, the Stanford team became the first to use LiDAR and complete the course. By the third challenge in 2007, five out of six finishing teams utilized Velodyne's mechanical LiDAR, thereby establishing the technology's foundational role in autonomous driving.

•Mechanical LiDAR Dominance:  Velodyne's 64-beam mechanically rotating LiDAR (such as the HDL-64E) became the standard sensor for early autonomous driving research and testing. While its 360-degree horizontal scanning capability was groundbreaking, the technology faced three major limitations: large size, high cost (up to $80,000 per unit), and  limited durability and reliability compared to automotive-grade products. 

•The Robotaxi Gold Rush: From 2009 to 2015,  pioneers like Google's self-driving project (later Waymo), Cruise and Zoox were founded, establishing robotaxi development as a global tech frontier. Their testing fleets grew to hundreds of vehicles, with the primary goal being technological development—where performance requirements for LiDAR far outweighed cost considerations. 

Phase 2: Pilot Operations and Solution Optimization (2016–2024)

During this period, Robotaxi transitioned from pure research and development to regional pilot operations. Concurrently, LiDAR technology saw significant diversification, while cost-effectiveness emerged as a critical challenge.

•Global Pilot Launches: 

In 2016, Uber launched a Robotaxi pilot in Pittsburgh, and NuTonomy introduced services in Singapore. In December 2018, Waymo rolled out the world’s first commercial autonomous taxi service, "Waymo One", in Phoenix. Chinese companies rapidly emerged, with Baidu Apollo, Pony.ai, WeRide, DiDi, and SAIC's Enjoy driving test and pilot programs in multiple global cities. 

•Performance Enhancement and Blind-Spot Coverage Needs: 

Pilot operations demanded higher performance from LiDAR. Beam counts increased from 64 to 128 (e.g., Hesai Pandar128, RoboSense Ruby Plus), with maximum detection ranges exceeding 250 meters. Blind-spot coverage LiDAR (e.g., RoboSense RS-Bpearl) gained widespread adoption to eliminate near-field blind zones, with typical configurations featuring "1 main LiDAR + 4 blind-spot LiDARs". 

•Advent of Semi-Solid-State Solutions and Cost Optimization: 

To address the high costs and reliability limitations of mechanical LiDAR, automotive-grade semi-solid-state LiDAR (e.g., Hesai AT128, RoboSense M1) entered the market. The mass adoption of LiDAR in passenger car ADAS after 2021 significantly improved its cost-effectiveness and durability. This progress enabled Robotaxi developers to replace a single mechanical LiDAR with a suite of four semi-solid-state units, achieving substantial cost savings.

Phase 3: Scaled Commercialization and the Digital Revolution (Starting 2025)

2025 marks the beginning of mass commercialization for Robotaxis, where advancements in policy frameworks and technology have collectively propelled the industry into a phase of ten-thousand-vehicle deployments.

Policy and Regulatory Breakthroughs: 

•China has introduced autonomous driving regulations to pave the way for mass commercialization. For example, the Beijing Autonomous Driving Vehicle Regulation implemented in April 2025 provides a legal basis for L3 autonomous vehicles to operate on public roads. 

•The U.S. Department of Transportation released a new automated vehicle regulatory framework in April 2025, further easing restrictions and streamlining approval processes.

Changes in Demand: From Performance-First to Holistic Considerations: 

Following the shift to scaled commercialization, Robotaxis are now managed as operational assets. Requirements for LiDAR have evolved toward a triad of high performance, low cost, and high reliability. Operators must avoid service interruptions or accidents due to hardware failures. Performance enhancements focused on perception upgrades, with automotive-grade (or higher) reliability emerging as a key requirement. 

Digital and Solid-State Technological Iteration: 

A new generation of automotive-grade LiDAR based on SPAD-SoC chip digital architectures has entered mass production. Beam counts have risen to over 500, significantly enhancing detection capability for small and low-lying obstacles (e.g., clearly imaging a 13x17 cm rock from 130 meters away). Fully solid-state blind-spot LiDARs (e.g., RoboSense E1) are gradually replacing mechanical LiDARs, offering greater reliability, smaller size, and cost reductions to the level of two hundred US dollars . 

Evolution of Mainstream Solutions:

The combination of "automotive-grade digital main LiDAR with >500-beam and fully solid-state digital blind spot LiDAR" is emerging as the new mainstream perception configuration for large-scale Robotaxi commercialization, replacing previous setups. Leading global players such as Waymo, Cruise, Baidu, and DiDi are already adopting this solution to upgrade their fleets.

Trends and Future Outlook

The future development of LiDAR and Robotaxi will exhibit the following characteristics: 

Technology: Solid-state and digitalization are clear trends. Fully solid-state LiDAR, with no moving parts, offers significant advantages in reliability, size, and cost potential, positioning it as the future mainstream direction. Leading manufacturers are adopting technologies like SPAD-SoC to achieve chip-level integration, reducing component counts and cutting production time by 95%, which significantly lowers costs and enhances reliability.  Meanwhile, digitalization enables main LiDAR systems to achieve resolutions exceeding 500-beam, improving detection of small objects and enhancing vehicle asset safety.

Cost: Continued Reduction drives broader adoption. Chinese manufacturers are leading a “cost revolution” in LiDAR, with high-performance solutions priced around $200 already announced and set for mass production by 2025. This will further accelerate the adoption of Robotaxi and ADAS functionalities.

Applications: Entering a “Dual-Track Driven” New Era. LiDAR is seeing broad application across the automotive and general robotics sectors. Emerging markets such as unmanned delivery, robotic lawn mowers, and humanoid robots are becoming a second growth curve for LiDAR companies. This, in turn, provides Robotaxi with a broader technology ecosystem and greater cost-reduction potential. In essence, Robotaxi benefits from synergistic industry growth driven by both automotive and robotics tracks.

Conclusion: Mutual Reinforcement for a Shared Future 

The development of LiDAR and Robotaxi has been driven by a powerful synergy between technological innovation and commercial application.

LiDAR as the "Eyes" of Robotaxis: It provides critical safety redundancy and precise perception capabilities for cross-scenario full autonomy, serving as an indispensable sensor for achieving L4 autonomy. 

Robotaxis as the "Catalyst" for LiDAR: The large-scale, demanding requirements of Robotaxi applications have driven rapid technological iteration in LiDAR, accelerating performance improvements and cost reduction while raising standards for reliability (automotive-grade, fully solid-state) and capability (high-beam digital architecture).

Looking back, from the initial applications in DARPA challenges, to technical optimization and cost control during the global pilot phase, to the rise of digital and solid-state solutions in the era of scale commercialization—Chinese LiDAR manufacturers have emerged as dominant global players, achieving a remarkable leap from latecomer to industry leader.

In the future, as LiDAR follows Moore's Law to achieve exponential performance gains and cost reductions at the chip level, it will not only facilitate larger-scale commercialization of Robotaxi and reshape human mobility, but also serve as a core perception component empowering the intelligent transformation of diverse industries. LiDAR will become a foundational infrastructure of the smart society of tomorrow.