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SpaceX will host Hyperloop Pod Competition next week, Jan 27-29, 2017

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Hyperloop test track outside of SpaceX
SpaceX Hyperloop Test Track (Jan.7, 2017) [Source: Teslarati via Marco Papa]

Get ready to see Hyperloop concept pods fire through the 1-mile test track located outside of SpaceX and Tesla’s Design Studio in Hawthorne, California, next week between January 27-29. Elon Musk and SpaceX first unveiled the idea for a new high-speed ground transport system called the Hyperloop on August 12, 2013 with the publication of a white paper, the Hyperloop Alpha Preliminary Design Study. SpaceX’s sponsored Hyperloop Pod Competition is an incentive prize competition created to inspire university students and independent engineering teams to design and build a subscale prototype transport vehicle (a “Hyperloop pod”) that will demonstrate technical feasibility of various aspects of the high speed transportation concept. To support this competition, SpaceX has constructed a test track outside of its headquarters which we had the opportunity to see during early construction last year.

There are three judging phases in the Hyperloop Pod competition: a design competition that was held in January 2016 and an on-track competition to be held January 27–29, 2017 (Competition Weekend I), followed by a Summer 2017 (Competition Weekend II). The original specification for the Competition Basic for the Design Weekend and the competition Weekend I, though no longer available at SpaceX, can still be found online.

DESIGN WEEKEND

The Design weekend was held in January 2016 at Texas A&M University. Awards were given in three categories:

SUBSYSTEM

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Best Overall Subsystem Award: Auburn University | Auburn University Hyperloop Team.

DESIGN ONLY

Top Design Concept Award: Universitat Politècnica de Valencia | Makers UPV Team

DESIGN AND BUILD CATEGORY OVERALL

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Massachusetts Institute of Technology | MIT Hyperloop Team

MIT Hyperloop Team’s design was awarded the “Best Overall Design Award”, among the 23 designs selected to move to the prototype stage. The design proposes a 250 kg (551 lb) pod with a carbon fiber and polycarbonate sheet exterior. It is elevated by a passive magnetic levitation system comprising 20 neodymium magnets that will maintain a 15 mm (0.6 in) distance above the track. The team says with air pressure at 140 Pascals, the pod could accelerate at 2.4 G and have 2 Newton aerodynamic drag when traveling at 110 m/s. The design includes a fail-safe braking system that automatically halts the pod should the actuators or computers fail, and low speed emergency drive wheels that can move the pod 1 m/s. Delft Hyperloop received a “Pod Innovation Award”, while Badgerloop at University of Wisconsin, Madison, Hyperloop at Virginia Tech, and HyperXite at UC Irvine each received a “Pod Technical Excellence Award.” The full list of Awards and news clips from the Design Weekend can be found at the Texas A&M University Engineering web site. Besides the winning teams, several other teams were invited to compete in the upcoming Competition Weekend I from the Design and Build category:

  • rLoop (Non-student team)
  • University of Waterloo | uWaterloo Hyperloop
  • University of Washington | UWashington Hyperloop
  • University of Toronto | University of Toronto
  • University of Maryland and Rutgers University | RUMD Loop
  • University of Florida | GatorLoop
  • University of of Colorado, Denver | Team HyperLynx
  • University of Cincinnati | Hyperloop UC
  • University of California, Santa Barbara | UCSB Hyperloop
  • University of California, Berkeley | bLoop
  • Texas A&M University | TAMU Aerospace Hyperloop
  • Technical University of Munich | WARR Hyperloop
  • Purdue University | Purdue Hyperloop Design Team
  • Oral Roberts University | Codex
  • Lehigh University | Lehigh Hyperloop
  • Keio University | Keio Alpha
  • Drexel University | Drexel Hyperloop
  • Carnegie Mellon University | Carnegie Mellon Hyperloop

In February 3, 2016 eight more teams advanced to Competition Weekend I.

  • Cornell University + Harvey Mudd College + University of Michigan + Northeastern University + Memorial University of Newfoundland(Canada) + Princeton University | OpenLoop
  • Louisiana State University | Bayou Bengals
  • New York University | NYU Hyperloop
  • RMIT University | VicHyper
  • John’s High School | HyperLift
  • University of Illinois at Urbana-Champaign | Illini Hyperloop
  • University of Southern California | USC Hyperloop
  • University of Wisconsin, Milwaukee | Mercury Three

In the end, 30 of the 115 teams that submitted designs in January 2016 were selected to build hardware to compete in Competition Weekend I. There were more than 1,000 applicants at earlier stages of the competition.

JUDGING CRITERIA

Originally, the second Phase of the competition was supposed to involve competitive runs in the Hyperloop test track to be awarded based on various classes (fully functional pod, susbsystem test pod, etc.) and pod mass. This phase of the competition was renamed“Competition Weekend I,” when SpaceX added a third phase of the competition, Competition Weekend II. The original SpaceX Hyperloop Pod Competition – Rules and Requirements for Weekend I  can be seen at the end of this article. We’ve embedded a copy of the original document from SpaceX.

The Judging Criteria are listed in the document, and involve scoring in 4 different categories, for a maximum overall total of 2500 points.

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  • Category 1: Final Design and Construction (500 points)
  • Category 2: Safety and Reliability (500 points)
  • Category 3: Performance in Operations (500 points)
  • Category 4: Performance in Flight (1000 points)

Competition Weekend I Judging Criteria – Source: SpaceX

HYPERLOOP TEST TRACK

AECOM, a company that has designed and built some of the world’s most impressive transportation systems, was selected to design and build the world’s first Hyperloop test track as part of the pod competition hosted by SpaceX

SpaceX Hyperloop Test Track (Jan.7, 2017) [Source: Teslarati via Marco Papa]

The track is a straight one-mile run on Jack Northrop Avenue, between Crenshaw Blvd. and Prairie Ave. The SpaceX Hyperloop test track — or Hypertube — was designed in 2015 and was constructed in the fall 2016, reaching its full length of one mile by October 2016. The test track’s six-foot diameter steel tube includes a non-magnetic sub-track and said to be capable of achieving 99.8 percent vacuum. The test track itself is also a prototype, where SpaceX anticipates learning from the design, build process and evaluates how to apply automated construction techniques to future Hyperloop tracks.

The Hypertube test track is designed to enable competitors who implement a wide array of designs and build pods that will test a variety of subsystem technologies that are important to new vehicle transport systems. This will include Hyperloop-specific pods—with air-bearing suspension and low-pressure compressor designs—as well as wheeled vehicle and magnetic levitation rail designs that will support a wide array of vehicle technologies to be tested. While the Design Weekend held at Texas A&M University was open to the public, it is unclear if the Competition Weekend I will be as well, or if it will be an invitation only event like many of the SpaceX and Tesla events. Several inquiries for tickets posted to the Twitter account of the Hyperloop Pod Competition went unanswered. The Official SpaceX Hyperloop Pod Competition page does not shed any light on who will be able to attend either.

HYPERLOOP POD COMPETITION II

According to SpaceX, “based on the high-quality submissions and overwhelming enthusiasm surrounding the competition, SpaceX is moving forward with a second installment of the competition: Hyperloop Pod Competition II, which will culminate in a second competition in Summer 2017 at SpaceX’s Hyperloop test track. Hyperloop Competition II will be focused on a single criterion: maximum speed. The second competition is open to new student teams interested in competing on the test track, as well as to existing student teams who have already built and tested Pods to further refine their designs.” The Competition Weekend II event will be held in the Summer 2017 at the same SpaceX Hyperloop test track.

[pdf-embedder url=”http://www.teslarati.com/wp-content/uploads/2017/01/spacex-hyperloop-competition-rules.pdf”]

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Tesla Full Self-Driving v14.2.2.5 might be the most confusing release ever

With each Full Self-Driving release, I am realistic. I know some things are going to get better, and I know some things will regress slightly. However, these instances of improvements are relatively mild, as are the regressions. Yet, this version has shown me that it contains extremes of both.

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Credit: Tesla

Tesla Full Self-Driving v14.2.2.5 hit my car back on Valentine’s Day, February 14, and since I’ve had it, it has become, in my opinion, the most confusing release I’ve ever had.

With each Full Self-Driving release, I am realistic. I know some things are going to get better, and I know some things will regress slightly. However, these instances of improvements are relatively mild, as are the regressions. Yet, this version has shown me that it contains extremes of both.

It has been about three weeks of driving on v14.2.2.5; I’ve used it for nearly every mile traveled since it hit my car. I’ve taken short trips of 10 minutes or less, I’ve taken medium trips of an hour or less, and I’ve taken longer trips that are over 100 miles per leg and are over two hours of driving time one way.

These are my thoughts on it thus far:

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Speed Profiles Are a Mixed Bag

Speed Profiles are something Tesla seems to tinker with quite frequently, and each version tends to show a drastic difference in how each one behaves compared to the previous version.

I do a vast majority of my FSD travel using Standard and Hurry modes, although in bad weather, I will scale it back to Chill, and when it’s a congested city on a weekend or during rush hour, I’ll throw it into Mad Max so it takes what it needs.

Early on, Speed Profiles really felt great. This is one of those really subjective parts of the FSD where someone might think one mode travels too quickly, whereas another person might see the identical performance as too slow or just right.

To me, I would like to see more consistency from release to release on them, but overall, things are pretty good. There are no real complaints on my end, as I had with previous releases.

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In a past release, Mad Max traveled under the speed limit quite frequently, and I only had that experience because Hurry was acting the same way. I’ve had no instances of that with v14.2.2.5.

Strange Turn Signal Behavior

This is the first Full Self-Driving version where I’ve had so many weird things happen with the turn signals.

Two things come to mind: Using a turn signal on a sharp turn, and ignoring the navigation while putting the wrong turn signal on. I’ve encountered both things on v14.2.2.5.

On my way to the Supercharger, I take a road that has one semi-sharp right-hand turn with a driveway entrance right at the beginning of the turn.

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Only recently, with the introduction of v14.2.2.5, have I had FSD put on the right turn signal when going around this turn. It’s obviously a minor issue, but it still happens, and it’s not standard practice:

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When sharing this on X, I had Tesla fans (the ones who refuse to acknowledge that the company can make mistakes) tell me that it’s a “valid” behavior that would be taught to anyone who has been “professionally trained” to drive.

Apparently, if you complain about this turn signal, you are also claiming you know more than Tesla engineers…okay.

Nobody in their right mind has ever gone around a sharp turn when driving their car and put on a signal when continuing on the same road. You would put a left turn signal on to indicate you were turning into that driveway if that’s what your intention was.

Like I said, it’s a totally minor issue. However, it’s not really needed, and nor is it normal. If I were in the car with someone who was taking a simple turn on a road they were traveling, and they signaled because the turn was sharp, I’d be scratching my head.

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I’ve also had three separate instances of the car completely ignoring the navigation and putting on a signal that is opposite to what the routing says. Really quite strange.

Parking Performance is Still Underwhelming

Parking has been a complaint of mine with FSD for a long time, so much so that it is pretty rare that I allow the vehicle to park itself. More often than not, it is because I want to pick a spot that is relatively isolated.

However, in the times I allow it to pull into a spot, it still does some pretty head-scratching things.

Recently, it tried to back into a spot that was ~60% covered in plowed snow. The snow was piled about six feet high in a Target parking lot.

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Tesla ends Full Self-Driving purchase option in the U.S.

A few days later, it tried backing into a spot where someone failed the universal litmus test of returning their shopping cart. Both choices were baffling and required me to manually move the car to a different portion of the lot.

I used Autopark on both occasions, and it did a great job of getting into the spot. I notice that the parking performance when I manually choose the spot is much better than when the car does the entire parking process, meaning choosing the spot and parking in it.

It’s Doing Things (For Me) It’s Never Done Before

Two things that FSD has never done before, at least for me, are slow down in School Zones and avoid deer. The first is something I usually take over manually, and the second I surprisingly have not had to deal with yet.

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I had my Tesla slow down at a school zone yesterday for the first time, traveling at 20 MPH and not 15 MPH as the sign suggested, but at the speed of other cars in the School Zone. This was impressive and the first time I experienced it.

I would like to see this more consistently, and I think School Zones should be one of those areas where, no matter what, FSD will only travel the speed limit.

Last night, FSD v14.2.2.5 recognized a deer in a roadside field and slowed down for it:

Navigation Still SUCKS

Navigation will be a complaint until Tesla proves it can fix it. For now, it’s just terrible.

It still has not figured out how to leave my neighborhood. I give it the opportunity to prove me wrong each time I leave my house, and it just can’t do it.

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It always tries to go out of the primary entrance/exit of the neighborhood when the route needs to take me left, even though that exit is a right turn only. I always leave a voice prompt for Tesla about it.

It still picks incredibly baffling routes for simple navigation. It’s the one thing I still really want Tesla to fix.

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Investor's Corner

Tesla gets tip of the hat from major Wall Street firm on self-driving prowess

“Tesla is at the forefront of autonomous driving, supported by a camera-only approach that is technically harder but much cheaper than the multi-sensor systems widely used in the industry. This strategy should allow Tesla to scale more profitably compared to Robotaxi competitors, helped by a growing data engine from its existing fleet,” BoA wrote.

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Credit: Tesla

Tesla received a tip of the hat from major Wall Street firm Bank of America on Wednesday, as it reinitiated coverage on Tesla shares with a bullish stance that comes with a ‘Buy’ rating and a $460 price target.

In a new note that marks a sharp reversal from its neutral position earlier in 2025, the bank declared Tesla’s Full Self-Driving (FSD) technology the “leading consumer autonomy solution.”

Analysts highlighted Tesla’s camera-only architecture, known as Tesla Vision, as a strategic masterstroke. While technically more challenging than the multi-sensor setups favored by rivals, the vision-based approach is dramatically cheaper to produce and maintain.

This cost edge, combined with Tesla’s rapidly expanding real-world data engine, positions the company to scale robotaxis far more profitably than competitors, BofA argues in the new note:

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“Tesla is at the forefront of autonomous driving, supported by a camera-only approach that is technically harder but much cheaper than the multi-sensor systems widely used in the industry. This strategy should allow Tesla to scale more profitably compared to Robotaxi competitors, helped by a growing data engine from its existing fleet.”

The bank now attributes roughly 52% of Tesla’s total valuation to its Robotaxi ambitions. It also flagged meaningful upside from the Optimus humanoid robot program and the fast-growing energy storage business, suggesting the auto segment’s recent headwinds, including expired incentives, are being eclipsed by these higher-margin opportunities.

Tesla’s own data underscores exactly why Wall Street is waking up to FSD’s potential. According to Tesla’s official safety reporting page, the FSD Supervised fleet has now surpassed 8.4 billion cumulative miles driven.

Tesla FSD (Supervised) fleet passes 8.4 billion cumulative miles

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That total ballooned from just 6 million miles in 2021 to 80 million in 2022, 670 million in 2023, 2.25 billion in 2024, and a staggering 4.25 billion in 2025 alone. In the first 50 days of 2026, owners added another 1 billion miles — averaging more than 20 million miles per day.

This avalanche of real-world, camera-captured footage, much of it on complex city streets, gives Tesla an unmatched training dataset. Every mile feeds its neural networks, accelerating improvement cycles that lidar-dependent rivals simply cannot match at scale.

Tesla owners themselves will tell you the suite gets better with every release, bringing new features and improvements to its self-driving project.

The $460 target implies roughly 15 percent upside from recent trading levels around $400. While regulatory and safety hurdles remain, BofA’s endorsement signals growing institutional conviction that Tesla’s data advantage is not hype; it’s a tangible moat already delivering billions of miles of proof.

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Tesla to discuss expansion of Samsung AI6 production plans: report

Tesla has reportedly requested an additional 24,000 wafers per month, which would bring total production capacity to around 40,000 wafers if finalized.

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Tesla-Chips-HW3-1
Credit: Tom Cross

Tesla is reportedly discussing an expansion of its next-generation AI chip supply deal with Samsung Electronics. 

As per a report from Korean industry outlet The Elec, Tesla purchasing executives are reportedly scheduled to meet Samsung officials this week to negotiate additional production volume for the company’s upcoming AI6 chip.

Industry sources cited in the report stated that Tesla is pushing to increase the production volume of its AI6 chip, which will be manufactured using Samsung’s 2-nanometer process.

Tesla previously signed a long-term foundry agreement with Samsung covering AI6 production through December 31, 2033. The deal was reportedly valued at about 22.8 trillion won (roughly $16–17 billion).

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Under the existing agreement, Tesla secured approximately 16,000 wafers per month from the facility. The company has reportedly requested an additional 24,000 wafers per month, which would bring total production capacity to around 40,000 wafers if finalized.

Tesla purchasing executives are expected to discuss detailed supply terms during their visit to Samsung this week.

The AI6 chip is expected to support several Tesla technologies. Industry sources stated that the chip could be used for the company’s Full Self-Driving system, the Optimus humanoid robot, and Tesla’s internal AI data centers.

The report also indicated that AI6 clusters could replace the role previously planned for Tesla’s Dojo AI supercomputer. Instead of a single system, multiple AI6 chips would be combined into server-level clusters.

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Tesla’s semiconductor collaboration with Samsung dates back several years. Samsung participated in the design of Tesla’s HW3 (AI3) chip and manufactured it using a 14-nanometer process. The HW4 chip currently used in Tesla vehicles was also produced by Samsung using a 5-nanometer node.

Tesla previously planned to split production of its AI5 chip between Samsung and TSMC. However, the company reportedly chose Samsung as the primary partner for the newer AI6 chip.

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