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Tesla’s race to autonomy: No one said it would be easy
Need to type up a quick memo before work? Forgot to eat breakfast before driving to school? In just a few years, driving may be a more hands-off endeavor than ever before if companies like Tesla, Uber, Volvo, Alphabet, General Motors, or Ford have anything to do about it. You could be a passenger in your own self-driving car, weaving in and out of traffic with ease and parallel parking like a pro every time. It seems like most every company even tangentially related to cars is pouring money into the race for autonomy.
The freedom of self-driving cars is still heavily dependent on regulatory whim and technological availability, but some are setting demanding goals in an effort to finish first in that race. Tesla for example, plans to showcase its Full Self-Driving Capability by driving one of its fleet cars from California to New York, without human involvement, by the end of this year. But their competitors are moneyed, motivated and many.
The Self-Driving Battle Arena
For Uber, success in autonomous driving research could be a sweet distraction from the recent troubles of the company. Its self-driving program has been based in Pittsburgh, right next to Carnegie Mellon with its highly regarded robotics program since it began in 2015. Then-CEO Travis Kalanick was determined to stay on top of the industry. “It starts with understand that the world is going to go self-driving and autonomous,” Kalanick said in a 2016 interview with Business Insider. “So if that’s happening, what would happen if we weren’t a part of that future? If we weren’t part of the autonomy thing? Then the future passes us by basically, in a very expeditious and efficient way.”
Plagued by lawsuits, investigations, and subsequent executive upheaval that saw Kalanick’s resignation from the enterprise he founded, Uber is still one of the best places for researchers and engineers to work on their projects. The company has armies of vehicles across the country, vast datasets of information from the millions of miles its cars have covered through its ride-hailing branch, and the money to fund its engineers’ work.
This does not mean that Uber’s self-driving program has remained untouched. Waymo, the autonomous car division of Google’s parent company, Alphabet, is currently suing Uber over files allegedly by Anthony Levandowski when he moved from Waymo to Uber. According to Reuters, in recent court filings, Waymo has claimed that Uber knew of the stolen intellectual property and even conspired with Levandowski to use it. Uber denies the allegations and actually fired Levandowski on May 30, claiming he had not cooperated with their internal investigation– and probably hoping to win some goodwill from the judge who has already said Waymo had produced a convincing case.
It is unlikely the scandals will affect the decisions of most researchers to stay with the company. As Wired’s Aarian Marshall points out, the long timeline of building a safe autonomous car makes engineers less likely to leave at a moment’s notice in a period of executive instability. And the branch’s position in Pittsburgh rather than Silicon Valley means the roiling news is less sensationalized and the researchers less affected. The ride-sharing company’s failure to live up to certain promises, including backing one of Pittsburgh’s federal grant proposals or hiring from neighborhoods near its test tracks, have drawn ire from many local activists and politicians, as reported by the New York Times. Even so, it has helped the city break away from its steel past and into a high-tech future.
Meanwhile, Uber’s main competitor in the ride-sharing industry, Lyft, has been making strides to continue chipping away Uber’s monopoly in any field, including self-driving cars, as Uber deals with scandal after scandal. As reported by Recode, Lyft is steadily gaining ground on Uber in terms of the share of ride-hailing app downloads as its ratings in the IOS App Store rise and Uber’s falls. This recent shift in market share comes as Waymo and Lyft start a new partnership that will combine Waymo’s advanced technology with Lyft’s vast amounts of data on people, where and how they drive. “Lyft’s vision and commitment to improving the ways cities move with help Waymo’s self-driving technology reach more people, in more places,” a Waymo spokesperson told Wired. Extending Waymo’s dataset beyond the few cities, including Phoenix and Pittsburgh, allows the enterprise to collect the small details of average people’s driving habits much faster and accurately than its test drives around Silicon Valley will.
But despite Waymo’s eight years of self-driving research, it still has to play catch up to Uber in some regards. Waymo just started testing autonomous trucks earlier this month, while Uber first used a self-driving truck to deliver a shipment last August, advancing its technology quickly after it snatched up the self-driving truck startup Otto—founded by Anthony Levandowski after he left Waymo— in January of 2016. Yet, Waymo has the benefit of its parent company’s huge cash reserves and data.
Growing Pains
Tesla is moving its autonomous program forward at an increasingly demanding pace, trying to meet that goal of driving from Los Angeles to New York by the end of this year. It, like Uber, is going through some executive shakeup: after just six months with Tesla, Chris Lattner, Vice President of its Autopilot Software program, left the company after reported tensions with Elon Musk. Tesla explained that the former Apple engineer was not a “good fit.” It stands to mention that working under Musk is notoriously a high-pressure gig. According to LinkedIn Insights, the average tenure of a Tesla employee is only 2.2 years, while companies like General Motors keeps its employees for almost 9. But Lattner’s exit is just one example of many of talented Tesla self-driving engineers leaving the company or being poached by the competition, like Waymo.
While Autopilot can do many impressive things— change lanes, brake before obstacles, and generally act as a rational human driver— it is far from perfect. The program is still technically in “public beta” testing, and rated by the National Transportation Safety Board as a 2 out of 5 on its scale of autonomy.
The fatal crash of a Model S owner Joshua Brown in May 2016 serves as a good reminder that drivers are cautioned to pay attention and keep their hands on the wheel at all times while using Autopilot. Tesla’s driving-assist feature, at the time, could not distinguish the difference between the bright sky and the white truck. Tesla and Autopilot were cleared of responsibility by the NTSB because Brown was given several warnings to take back control of the wheel. But it is a poignant example that Autopilot does not function as a self-driving car and still requires a driver’s full attention. After the accident, Tesla was forced to start developing its own hardware for Autopilot. Mobileye, which previously supplied Tesla’s image processing chips, ended its partnership in a public spat with Musk.
According to Lattner’s public resume, the transition to its own hardware presented “many tough challenges” to the Tesla team. Musk commented to shareholders in June that Tesla is “almost there in terms of exceeding the ability” of the original hardware. All of Tesla’s vehicles in production, including the upcoming Model 3, have the capability to engage Autopilot (for a price) and the necessary hardware to enable full self-driving someday. Autopilot will continue using the camera-based system that Tesla swears by, even as most of the industry focuses on developing LiDAR technology based on light and lasers.
And while Tesla prefers to work mostly alone, the rest of the industry is also pairing up, making deals, partnerships, and contracts between manufacturers, data giants, and service teams. Musk is taking a move out of Steve Jobs’ playbook by vertically integrating everything within the business, from top-to-bottom. Waymo and Honda, Lyft and Waymo, Autoliv and Volvo, Hertz and Apple, Intel and Mobileye, Audi and NVIDIA, and almost every other combination you could think of. Predictions for when the first company will reach the finish line range from within a year to two decades from now. And even if the car is made, there is still the question of if cities and states will allow autonomous vehicles to drive on their streets. The technology is closer than ever, but for now, please keep your eyes on the road.
Tesla has launched a direct campaign targeting its customers in New Jersey, sending emails that warn of pending legislation that could effectively block true driverless technology in the state.
The email focuses on Senate Bill S.1677 and Assembly Bill A.3968, measures intended to create a three-year autonomous vehicle pilot program but laden with requirements that Tesla argues make unsupervised Robotaxis impossible.
Tesla is sending out this email to New Jersey Tesla owners, warning them that NJ could block autonomous vehicles, and to take action.
“Proposed legislation moving through Trenton right now would impose restrictions so severe that true driverless deployment would remain illegal.… pic.twitter.com/2bmY646AUL
— Sawyer Merritt (@SawyerMerritt) June 16, 2026
According to the email, the bills impose “restrictions so severe that true driverless deployment would remain illegal.” Specific hurdles include mandates for human safety drivers during operations, multimillion-dollar insurance minimums, reportedly $5 million, and thresholds like 100,000 miles of demonstrated safe autonomous driving before any driverless approval.
Tesla contends these are arbitrary barriers that ignore real-world performance data and favor entrenched competitors over innovative technologies like its Full Self-Driving (FSD) system.
The push comes as Tesla has started expanding Robotaxi operations in states like Texas, where unsupervised vehicles are already providing rides in several cities. New Jersey, by contrast, risks falling behind. The company highlights in the email communication that more than 94 percent of serious crashes result from human error, meaning impairment, distraction, or fatigue. These are all problems that Robotaxis eliminate entirely.
In 2025, New Jersey recorded 582 traffic deaths, underscoring the human cost of delayed adoption.
Tesla’s outreach stresses the transformative potential of robotaxis. For families, they could offer safer school runs without drowsy or distracted drivers. For seniors and people with disabilities, robotaxis promise independence and reliable mobility.
In areas with limited public transit, they could deliver affordable, on-demand transportation, reducing congestion, emissions, and overall transportation costs. Economically, the company warns that restrictive rules could cost New Jersey jobs, innovation investment, and billions in potential growth as autonomous ride-hailing scales elsewhere.
Supporters of the legislation, including Sen. Andrew Zwicker, describe the pilot as a cautious framework with strong safety oversight, including incident reporting, expert task forces, and restrictions in sensitive zones like school areas. They view it as balancing innovation with public protection.
Tesla and pro-AV advocates counter that the bill lacks technology neutrality, creates insurmountable entry barriers for commercial deployment, and prioritizes process over outcomes — effectively functioning as a de facto ban on services like Robotaxi.
This latest clash echoes Tesla’s past battles in New Jersey over direct vehicle sales. The email directs owners to Tesla’s advocacy platform, where they can send customized messages to legislators calling for amendments: outcome-based safety standards, open competition, and clear pathways for fully driverless commercial operations.
As hearings approach, Tesla’s campaign frames the issue as a choice between protecting the status quo and embracing life-saving progress. With robotaxi technology already proving itself in permissive states, New Jersey owners are being asked to ensure their state doesn’t lock out the future of transportation.
Turn-by-turn navigation is not new technology.
For over two decades, drivers have relied on Garmin, TomTom, and later smartphone apps like Google Maps and Waze to receive precise, reliable directions. These systems have guided millions safely through unfamiliar cities, highways, and backroads with remarkable effectiveness. They handle real-time traffic, construction detours, and complex intersections with minimal fuss.
Yet Tesla, the company that promised revolutionary Full Self-Driving (FSD), continues to struggle with this foundational capability. As FSD (Supervised) v14.3.4 has started rolling out to cars this week, navigation remains its glaring Achilles’ heel, undermining the entire autonomous vision.
Tesla Summon got insanely good in FSD v14.3.2 — Navigation? Not so much
Tesla’s FSD excels in many driving behaviors—smooth acceleration, confident lane changes in ideal conditions, and responsive handling of visible obstacles. However, when it comes to following a route accurately, the system falters repeatedly.
Owners report wrong turns, missed exits, inefficient routing through local roads instead of highways, phantom speed limit errors, and even directing vehicles to building rear entrances. Interventions for navigation issues often outnumber those for core driving maneuvers. Tesla has begun surveying owners specifically about these errors, acknowledging the problem after years of complaints.
Navigation is perhaps my biggest complaint when it comes to FSD, because sometimes, we do know better. Some of us have been living in our areas for our entire lives, but even those who have not have years or even decades of experience driving on local roads. We might know a little better about routing.
But the navigation mistakes are more than just FSD potentially taking a slightly different route that may or may not save you a few minutes. Sometimes, they’re genuinely mind-boggling.
This isn’t just annoying; it cascades into broader failures. A flawed route plan confuses the AI’s decision-making, leading to hesitant behavior, unnecessary disengagements, or dangerous maneuvers like attempting impossible U-turns or ignoring clear ramps. In a system meant to operate with minimal supervision, unreliable navigation erodes trust.
More often than not, false or plain incorrect navigation is what causes me to interrupt FSD operation. Unfortunately, I believe the latest FSD version is the worst example of it, and it leads me to believe that Tesla might be making some changes; they’ve just made them in the wrong direction.
It makes you wonder: Why is a company that has done so much with the progress of FSD and autonomy struggling so much with navigation, something that is not new and has been around a long time?
Multiple Data Sources
First, Tesla’s navigation relies on a fragile patchwork of multiple data sources—Google Maps, TomTom, OpenStreetMap, Valhalla, and its own fleet-derived data—stitched together rather than a single authoritative map. When these conflict on lane geometry, road status, or turn details, the system hesitates or chooses incorrectly.
Traditional GPS providers maintain centralized, regularly validated databases with professional curation and rapid updates. Tesla’s hybrid approach, while innovative in crowdsourcing, introduces inconsistencies that a purely vision-based or end-to-end AI approach may not easily reconcile in real time.
Persistent Learning
FSD seems to struggle with persistent learning from driver interventions.
Unlike consumer apps that quickly adapt to repeated corrections or user preferences (e.g., avoiding certain routes or remembering habitual detours), Tesla’s FSD often fails to internalize fixes on the same trip or across similar scenarios. Owners note making the same manual override multiple times without the routing engine updating its behavior meaningfully.
This stems from the neural architecture prioritizing real-time perception and control over long-term route memory and personalization, making navigation feel rigid and “opinionated” compared to the adaptive logic in Waze or Google Maps.
I noticed that when I asked Grok to try and get me home a certain way (a way that FSD routinely took in the past because it was the most efficient), it had to place a waypoint between my location at the time and my house. When I went to edit the waypoint out, as Grok had placed it for a way to get FSD to get off the highway at the right exit, it was stumped again, rerouted, and took a longer way home.
The next thing I’ve noticed, and this might be controversial, is that Nav has gotten even worse.
I think that might actually be a good thing; Tesla seems to be adjusting it. They just need to adjust it the opposite way.
The car is taking extremely strange routes to very… https://t.co/UHg3tVfNA2
— TESLARATI (@Teslarati) June 16, 2026
Reasoning, Scaling, and Intuition
Third, scaling navigation for unsupervised or robotaxi ambitions requires not just accuracy but adaptability and user-like reasoning. Current FSD often defaults to single routes that ignore driver preferences or real-world nuances like time-of-day traffic patterns. It fails to match the intuitive, context-aware planning that traditional systems have refined over the years.
Resolving navigation is critical for several reasons. Practically, it is the backbone of any autonomous journey: without trustworthy routing, the car cannot reliably reach destinations, rendering FSD useless for robotaxis or hands-free commutes. Safety depends on it—mismatched plans create hesitation in merges or intersections, increasing accident risk.
Economically, Tesla’s valuation and future hinge on FSD delivering unsupervised driving; persistent navigation flaws delay regulatory approval and erode consumer confidence. For owners who paid premiums for FSD, these issues represent unfulfilled promises. While it is unlikely Tesla will lose too many customers due to bad navigation, some will be frustrated with the constant need for human input.
Tesla has achieved miracles in electric vehicles and battery tech. Mastering turn-by-turn—technology Garmin nailed in the early 2000s—should not be this hard. By investing in tighter data integration, faster learning loops from interventions, and more intuitive routing algorithms, Tesla could close this gap.
Until then, FSD’s navigation struggles highlight a humbling truth: even the most ambitious innovator must sometimes master the basics before conquering the future.
A Tesla Cybertruck driver in the United Kingdom had their all-electric pickup seized by local police in the Greater Manchester area after the department cited “legitimate concerns.”
Last Thursday, police saw the pickup on the roads and decided to pull the driver over. Greater Manchester Police said:
“Whilst this may seem trivial to some, legitimate concerns exist around the safety of other road users or pedestrians if they were involved in a collision with the Cybertruck.”
🚨 A Tesla Cybertruck, which is illegal to drive in the UK due to safety concerns, has been seized by police in Greater Manchester
“Whilst this may seem trivial to some, legitimate concerns exist around the safety of other road users or pedestrians if they were involved in a… pic.twitter.com/cqhdPok3DM
— TESLARATI (@Teslarati) June 16, 2026
The Cybertruck in question was, according to the BBC, registered and insured abroad and was confiscated. The driver, who is a UK resident, was reported.
The Greater Manchester Police Department then added:
“The Tesla Cybertruck is not road-legal in the UK and does not hold a certificate of conformity.”
The Cybertruck cannot be legally driven in the UK because it has no UK Type Approval for operation in the country. This is due to some safety concerns, which are related to its angular shape and design. The stainless steel exoskeleton has sharp edges and projections that violate UK/EU rules on pedestrian protection.
Tesla has considered creating what it referred to as an “international version” that would be approved for operation in Europe. However, there has been no real movement on that front by the company, as it has been focused on the Robotaxi rollout primarily.
Tesla urges New Jersey owners to oppose new bill that could block Robotaxi
Tesla’s Navigation Nightmare: Why the easiest part of FSD might be the hardest
