Tesla vs. Waymo, and Hyundai's 1 Trillion Won Dream

In several U.S. cities, robotaxis with empty driver seats are no longer an unusual sight. Yet, the steering wheel in my own car is still in my hands. The difference isn’t hardware—it’s data.

When I first wrote this in November 2025, I examined the battle between the two giants, Waymo and Tesla, and the situation for Hyundai trailing behind them. In the ten months since, the landscape has shifted significantly. Waymo has doubled its weekly ride count, Tesla FSD has reached v14.3, and the founder of 42dot has stepped down from the Hyundai side. Autonomous driving is also a microcosm of the Big Tech AI investment war. As of September 2026, I am revisiting the same question.

Why Waymo won’t give up on LiDAR

Waymo’s principle is to never trust any single sensor 100%. The 5th-generation Waymo Driver utilizes LiDAR that sees up to 300 meters, cameras, and imaging radar that reads speeds even in poor weather. Even if cameras are obscured by heavy rain, the LiDAR and radar act as a backup.

Waymo’s strength lies in its ability to prove this persistence with numbers. They released results, verified by insurers like Swiss Re, showing 92% fewer pedestrian injuries and 85% fewer severe injury accidents compared to human drivers. While Tesla also provides statistics—one accident per 6.69 million miles with Autopilot engaged—it differs from Waymo because it is an internal report rather than third-party verified. This difference was also highlighted in the Tesla safety debate following the Miami ruling. The key to persuading tricky urban environments and regulators wasn’t just miles driven, but ‘proven safety.’

The result of this persuasion is reflected in ride counts. Paid rides, which were 250,000 per week at the end of 2025, surpassed 500,000 weekly rides in the first half of 2026, with service expanded to over 10 cities. Waymo is aiming for 1 million weekly rides by the end of 2026 and has secured 16 trillion won in investment. They hit a slight bump in the summer when they paused highway driving for a software recall, which also shows how carefully they manage their ‘proven safety’ brand.

Tesla FSD: How far can cameras alone take it?

Tesla takes the opposite path. They call LiDAR an “expensive and useless crutch,” removing it in favor of 8 cameras and neural networks that learn to drive entirely by mimicking humans.

In the past, developers wrote 300,000 lines of C++ code for decisions like “there is a person → predict their position in 3 seconds → press the brake.” Now, they feed millions of real-world driving videos into a neural network and let it learn on its own. This is the end-to-end approach, fueled by driving data sent in real-time from Tesla vehicles worldwide.

FSD v14, released in October 2025, featured a new architecture with a model size 10 times larger. The problem is that it is optimized for the latest HW4, effectively stalling performance for owners of older HW3 cars. As of September 2026, FSD has reached v14.3.9, with a newly designed vision encoder and AI compiler boosting response speeds by about 20%. While ‘FSD v14 Lite’ with reduced performance is being rolled out to HW3, the dynamic that top-tier performance is exclusive to HW4 remains.

The focus of functionality has also shifted. Upon reaching a destination, you can choose where to get off—a parking lot, a driveway, or a parking garage—and route setting is integrated into the neural network, allowing for immediate detours if the road is blocked. Slowing down and yielding for ambulances or police cars is now a standard feature. It is transitioning from a lane-keeping device to a personal assistant that completes tasks from destination to destination.

User videos have captured the car navigating to the 5th floor of a parking garage to find an exit, or subtly reversing to give space to pedestrians. Such behaviors cannot be hard-coded. They are the result of learning from millions of videos that this is the pattern humans follow in such situations.

Waymo vs. Tesla: 2026 Autonomous Driving Philosophy Comparison

Category	Waymo	Tesla
Core Philosophy	Proven Safety (Machines must be more perfect than humans)	Scalable AI (Evolving by learning like a human)
Sensor Suite	Redundant LiDAR+Camera+Radar	8 Cameras, Vision Only
AI Approach	Modular + Sensor Fusion	End-to-End (FSD v14)
Main Market	L4 Robotaxi (500k weekly rides, 10+ cities)	L2+ Driver Assist (FSD Subscription, millions of cars)
Safety Proof	Insurer verification & research papers	Focused on internal reports

Why is Chinese autonomous driving so fast?

While the two U.S. companies struggle with technical reliability and AI design, China has taken the shortcut of deregulation. Baidu’s Apollo Go recorded 2.2 million fully driverless rides in Q2 2025 alone, a 148% increase from the same period the previous year. Policies that alleviate accident liability have supported this growth, and the industry calls this regulatory arbitrage.

Smartphone company Xiaomi is the most threatening new entrant. By announcing an end-to-end framework called ORION, they have rapidly followed in Tesla’s footsteps. However, there are dark clouds. In March 2025, 3 people died in a crash while driver-assist was on, and in August, they recalled over 110,000 vehicles due to software defects. This is the price of the speed race.

Companies holding data without building cars

Nvidia is no longer just a GPU company. It has become a platform selling bundled autonomous driving chips, software, and complete AI models, and has won the CVPR Autonomous Driving Challenge for three consecutive years. The trend of deepening dependence on the Nvidia ecosystem in the semiconductor industry is repeating itself in autonomous driving. The weaker an automaker’s own software capabilities, the easier it is to be locked into this ecosystem.

Mobileye has an opposite strategy. They send only key information like lane lines and signs—10kb per km—from over 200 million vehicle cameras worldwide to update high-definition maps in real-time. Instead of collecting entire videos, they collect only the necessary data in a lightweight manner.

GM learned the most expensive lesson. Cruise, which had poured in over 10 trillion won, was dissolved in December 2024 after an accident in October 2023 where it hit a pedestrian and dragged them for 6 meters. It was a sign that the scaling costs of L4 robotaxis were difficult to sustain.

GM shifted its focus to L3 ’eyes-off’ for personal vehicles, starting real-road testing on California and Michigan highways in March 2026. Cumulative mileage for Super Cruise has exceeded 1 billion miles, and plans are underway to add a system combining LiDAR, radar, and cameras to the 2028 Cadillac Escalade IQ. However, the criticism that consumers view Super Cruise as far below FSD due to AI judgment rather than sensors remains valid. The structure of introducing highway-only features in 2028 despite using expensive hardware highlights GM’s cost-to-performance issue.

There are others quietly eyeing the market. Zoox, an Amazon subsidiary, started from scratch with purpose-built vehicles lacking steering wheels and pedals, and launched a commercial service in Las Vegas. It is evaluated as a potential dark horse if combined with Amazon’s logistics network.

Hyundai’s 42dot: Why the 1 trillion won dream faltered

In my first article, I pointed out that 42dot, which Hyundai Motor Group acquired for 1 trillion won, was aiming for a Tesla-style autonomous driving system without HD maps, so-called ‘Atria AI.’ The problem was fuel. While Tesla collects data in minutes, 42dot had to make do with 170,000 km of cumulative shuttle mileage as of June 2024. It was a scale that could serve as a sample, but hardly a dataset to train an AI.

The most painful evidence was user reviews. One Hyundai owner complained that on a specific stretch of the I-95 in the U.S., the car “every time” lost the lane and headed toward the guardrail. The word ’every time’ is the key. Without a pipeline to collect, analyze, and retrain on failure data, the same mistakes are repeated.

There was research capability. 42dot placed 2nd in the 2023 CVPR challenge, but the winner that year was Nvidia. They were just an excellent participant, not the one setting the rules. On top of this, Motional, the subsidiary handling L4 robotaxis, was following in Cruise’s footsteps with a cumulative loss of 2.3 trillion won, compounded by the confusion of juggling both L4 and L2+ fronts simultaneously.

This gap led to cracks in the organization. The founder of 42dot resigned in December 2025 following poor performance and conflicts over technical direction. A month later, Hyundai Motor Group recruited Dr. Minwoo Park, who had handled autonomous driving recognition technology at Tesla and Nvidia, as the new CEO. It was a choice to address the data shortage with someone who knows data best.

Changes are slowly appearing. On September 11, 2026, Hyundai Motor Group held an autonomous driving media day at 42dot’s headquarters in Pangyo and unveiled a video of an Ioniq 6 navigating downtown Seoul without driver intervention using only 8 cameras and fisheye lenses for the first time. CEO Minwoo Park placed the ‘data flywheel’ at the center of competitiveness, saying, “We will reverse the data disadvantage within 5 years.”

A roadmap has also been released: a two-track approach of Nvidia-based Level 2+ in the first half of 2028, Level 2++ in the second half of that year, and mass production of Level 2++ based on their own Atria AI in the second half of 2029.

To be cold-headed, the primary goal until 2028 is a method of buying and installing Nvidia’s complete solutions. The risk of software dependence has actually been formalized. Still, it is different from a year ago in that they have set a deadline for their own technology in 2029 and have made the collection of real-road data the central task of the organization in the meantime.

How will the autonomous driving war unfold after 2026?

The L4 robotaxi market has narrowed to a two-horse race between Waymo and Baidu with Cruise’s exit. It is a confrontation between those who have proven safety and those who have scale. The L3 consumer market has become the hottest battlefield with GM’s entry. The underlying principles are the same. Tesla has already made real-time driving data, Waymo has L4 safety data, and Mobileye has lightweight map data into moats. The debate over sensor selection was covered separately in Waymo vs. Tesla’s Sensor War.

For Hyundai Motor Group, there is now a hard deadline with numbers attached. They will survive with Nvidia solutions until 2028, and build their own technology by the second half of 2029 with the data accumulated in the meantime. The question is whether the experiment with a few Ioniq 6s running in Pangyo can grow at a speed that catches up with the millions of Tesla data points within 3 years. What Nokia and Kodak missed was not technology, but timing. Whether Hyundai’s timetable can avoid that lesson will be confirmed with the first mass-produced car in 2028.