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Self-driving Teslas and autonomous vehicles will end traffic as we know it
We are all fascinated with autonomous driving in terms of what it can do for us. Make the elderly mobile again without endangering the rest of us with their arguably reduced reaction times, less acute hearing and vision. We dream of the day when we can sleep through a long, boring trip. Doing valuable work in what would otherwise be dead time is a plus too. One thing we haven’t talked about too much is how autonomous cars can radically reduce the congestion of our roads.
Six Inches of Separation (With All Due Respect to Kevin Bacon)
One way we can reduce highway congestion is to reduce the following distances between cars. It takes a human about four seconds to react to a car stopping ahead of us. At 60 mph, that translates to 88 feet per second or a total traveled of 352 feet before you are really starting to stop the car. Using the 2 1/2 second rule would yield 220 feet. Now if you have a car which reacts in, oh say, 1,000 nanoseconds, or a millionth of a second, some have argued that a six inch separation would be more than enough time for the computer to stop the car in time to avoid a collision. So, a non-autonomous car would take up about 220 feet of roadway per car, autonomous cars would take up roughly 20 feet per car. 220 divided by 20 yields about 11 cars per 220 feet of roadway rather than one. You’ve magically increased the carrying capacity which decreases congestion.
Platooning
This increased use of autonomy will almost certainly create “platooning” on our roads where cars headed in the same direction are pulled up within inches of the car ahead creating a “car train” of 30, 50, or more cars all traveling at high speed to a destination ahead of them. With level 5 autonomy, some have suggested that 90 mph is reasonable while remaining very safe.
So let’s do a mind experiment here. You have a 220 foot stretch of roadway which can now safely carry 1 car traveling at 60 mph. Let’s put in a platoon of 11 cars traveling at 90 mph. That 220 foot stretch of roadway at 90 mph can carry 15 cars rather than 11 because 90 is 150% of 60. You have now increased the carrying capacity of the roadway by 1500%, or put another way, it would be like the New York State Thruway had 1/15 the cars on it that it does now. Rush hour would be like driving at three in the morning.
You may say that 220 feet is a preposterous amount of road and that people routinely travel only 10 to 20 feet behind the car in front of them. My response is look at the accident statistics. Yeah, you can travel that close. You just can’t travel that close safely.
Goose it Man!
One of the arguments against high speed travel in cars has been that as you increase speed, miles per kilowatt drop radically. Wind resistance is the big thief of range. When you read about people who manage to get ridiculous miles per charge out of their Teslas you can bet that last dollar that they are driving slowly!
Here’s where we can take a lesson from NASCAR and…wait for it, GEESE! Any fan of NASCAR knows that the drivers “draft” the car in front of them to save gas. The reason is very simple. The car in front is pushing the air out of the way, and the car behind benefits from traveling at the same speed in a partial vacuum, enabling the following driver to save fuel and possibly avoid a pit stop.
Why am I talking about geese? Ever wonder why geese travel in that cool V-formation? Similar reason. They avoid the turbulence from the goose ahead and conserve energy. Being cooperative sorts they trade places with the leader, who drops back and lets the next goose in line take over the toughest place, which is the lead. That way all the geese get to where they’re going quicker and with less fatigue. In our terms, with less battery energy expended.
I foresee platooning supplemented with leader “dropback” like the geese, let’s say, every five miles, to enable very fast driving times with lower fuel/kilowatt hour consumption. This will become part of the autonomous software suite.
So, all hail the goose, and I, for one, look forward to autonomous driving because of the effect platooning will have on our drives, and the automatic increase of the carrying capacity of our roads. Cool, very cool!
Allan Honeyman
(Submitted via email to the Teslarati Network. Do you a post you’d like to share? Email it to us at info@teslarati.com)
Elon Musk
The Boring Company just doubled its tunneling power in Nashville
The Boring Company’s Prufrock MB2 is commissioned and ready to mine beneath Nashville’s streets.
The Boring Company’s second tunnel boring machine, Prufrock MB2, is officially ready to dig in Nashville. The company confirmed the news on X, posting: “Prufrock-MB2 is ready to mine in Nashville! MB2 commissioning is complete, including the brief 11 rpm rotation shown here. Will MB2 catch up to MB1, who had quite the head start? And Prufrock-MB3 ships in August!”
MB2 arrives with meaningful improvements over its predecessor. Lessons learned from the launch and operation of MB1 have already been applied to MB2 to improve efficiency and prepare the machine for launch.
Traditional tunnel boring machines operate in a stop-and-go cycle, digging roughly five feet, halt, erect precast concrete segments to line the tunnel wall, then resume. That repeated interruption is one of the main reasons conventional tunneling is slow and expensive. Prufrock is designed to install the tunnel liner simultaneously with mining, eliminating the need to stop every five feet. The machine also skips the need for excavated launch pits. Prufrock arrives on a truck, tilts down, and launches into the ground within 24 hours. And when the tunnel is complete, it emerges from the ground and drives to its next launch site on a trailer, eliminating the need for expensive cranes or pit excavation. The machine is also fully electric and runs with zero people in the tunnel during normal operations, controlled remotely from a surface operations center.
Prufrock-MB2 is ready to mine in Nashville! MB2 commissioning is complete, including the brief 11 rpm rotation shown here.
Will MB2 catch up to MB1, who had quite the head start?
And Prufrock-MB3 ships in August! pic.twitter.com/TTrMql2aRg
— The Boring Company (@boringcompany) June 17, 2026
It won’t be long before we hear of another major update on The Boring Company’s Music City Loop project – a planned underground transit network beneath Nashville that would move passengers in electric vehicles through a series of tunnels at highway speeds, and bypassing surface traffic entirely. Nashville was selected in part because of its strong rock conditions that suits the Prufrock machines well, and relatively less regulatory hurdles.
Progress has been steady on multiple fronts. All 37 permits and approvals required ahead of tunneling have been obtained, out of 45 total. Key wins include a fully executed TDOT tunnel permit authorizing 25 miles of tunnel, unanimous airport authority approval for a Nashville International Airport station, and the city’s first residential station agreement serving downtown tower residents.
With MB1 already tunneling, MB2 now commissioned, and MB3 shipping in August, Nashville is becoming something of a live proving ground for scaled tunnel boring. The broader ambition is not limited to one city. The Boring Company’s stated goal is to make underground transportation a practical alternative to surface roads across major metro areas. Nashville is one of many cities, including a successful Las Vegas tunnel system, where that idea is being put to the test at real speed.
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.
The Boring Company just doubled its tunneling power in Nashville
Tesla urges New Jersey owners to oppose new bill that could block Robotaxi
