News
SpaceX will host Hyperloop Pod Competition next week, Jan 27-29, 2017
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
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
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.
- 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)
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
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”]
Elon Musk
Tesla AI Head says future FSD feature has already partially shipped
Tesla’s Head of AI, Ashok Elluswamy, says that something that was expected with version 14.3 of the company’s Full Self-Driving platform has already partially shipped with the current build of version 14.2.
Tesla and CEO Elon Musk have teased on several occasions that reasoning will be a big piece of future Full Self-Driving builds, helping bring forth the “sentient” narrative that the company has pushed for these more advanced FSD versions.
Back in October on the Q3 Earnings Call, Musk said:
“With reasoning, it’s literally going to think about which parking spot to pick. It’ll drop you off at the entrance of the store, then go find a parking spot. It’s going to spot empty spots much better than a human. It’s going to use reasoning to solve things.”
Musk said in the same month:
“By v14.3, your car will feel like it is sentient.”
Amazingly, Tesla Full Self-Driving v14.2.2.2, which is the most recent iteration released, is very close to this sentient feeling. However, there are more things that need to be improved, and logic appears to be in the future plans to help with decision-making in general, alongside other refinements and features.
On Thursday evening, Elluswamy revealed that some of the reasoning features have already been rolled out, confirming that it has been added to navigation route changes during construction, as well as with parking options.
He added that “more and more reasoning will ship in Q1.”
🚨 Tesla’s Ashok Elluswamy reveals Nav decisions when encountering construction and parking options contain “some elements of reasoning”
More uses of reasoning will be shipped later this quarter, a big tidbit of info as we wait v14.3 https://t.co/jty8llgsKM
— TESLARATI (@Teslarati) January 9, 2026
Interestingly, parking improvements were hinted at being added in the initial rollout of v14.2 several months ago. These had not rolled out to vehicles quite yet, as they were listed under the future improvements portion of the release notes, but it appears things have already started to make their way to cars in a limited fashion.
Tesla Full Self-Driving v14.2 – Full Review, the Good and the Bad
As reasoning is more involved in more of the Full Self-Driving suite, it is likely we will see cars make better decisions in terms of routing and navigation, which is a big complaint of many owners (including me).
Additionally, the operation as a whole should be smoother and more comfortable to owners, which is hard to believe considering how good it is already. Nevertheless, there are absolutely improvements that need to be made before Tesla can introduce completely unsupervised FSD.
Elon Musk
Tesla’s Elon Musk: 10 billion miles needed for safe Unsupervised FSD
As per the CEO, roughly 10 billion miles of training data are required due to reality’s “super long tail of complexity.”
Tesla CEO Elon Musk has provided an updated estimate for the training data needed to achieve truly safe unsupervised Full Self-Driving (FSD).
As per the CEO, roughly 10 billion miles of training data are required due to reality’s “super long tail of complexity.”
10 billion miles of training data
Musk comment came as a reply to Apple and Rivian alum Paul Beisel, who posted an analysis on X about the gap between tech demonstrations and real-world products. In his post, Beisel highlighted Tesla’s data-driven lead in autonomy, and he also argued that it would not be easy for rivals to become a legitimate competitor to FSD quickly.
“The notion that someone can ‘catch up’ to this problem primarily through simulation and limited on-road exposure strikes me as deeply naive. This is not a demo problem. It is a scale, data, and iteration problem— and Tesla is already far, far down that road while others are just getting started,” Beisel wrote.
Musk responded to Beisel’s post, stating that “Roughly 10 billion miles of training data is needed to achieve safe unsupervised self-driving. Reality has a super long tail of complexity.” This is quite interesting considering that in his Master Plan Part Deux, Elon Musk estimated that worldwide regulatory approval for autonomous driving would require around 6 billion miles.
FSD’s total training miles
As 2025 came to a close, Tesla community members observed that FSD was already nearing 7 billion miles driven, with over 2.5 billion miles being from inner city roads. The 7-billion-mile mark was passed just a few days later. This suggests that Tesla is likely the company today with the most training data for its autonomous driving program.
The difficulties of achieving autonomy were referenced by Elon Musk recently, when he commented on Nvidia’s Alpamayo program. As per Musk, “they will find that it’s easy to get to 99% and then super hard to solve the long tail of the distribution.” These sentiments were echoed by Tesla VP for AI software Ashok Elluswamy, who also noted on X that “the long tail is sooo long, that most people can’t grasp it.”
News
Tesla earns top honors at MotorTrend’s SDV Innovator Awards
MotorTrend’s SDV Awards were presented during CES 2026 in Las Vegas.
Tesla emerged as one of the most recognized automakers at MotorTrend’s 2026 Software-Defined Vehicle (SDV) Innovator Awards.
As could be seen in a press release from the publication, two key Tesla employees were honored for their work on AI, autonomy, and vehicle software. MotorTrend’s SDV Awards were presented during CES 2026 in Las Vegas.
Tesla leaders and engineers recognized
The fourth annual SDV Innovator Awards celebrate pioneers and experts who are pushing the automotive industry deeper into software-driven development. Among the most notable honorees for this year was Ashok Elluswamy, Tesla’s Vice President of AI Software, who received a Pioneer Award for his role in advancing artificial intelligence and autonomy across the company’s vehicle lineup.
Tesla also secured recognition in the Expert category, with Lawson Fulton, a staff Autopilot machine learning engineer, honored for his contributions to Tesla’s driver-assistance and autonomous systems.
Tesla’s software-first strategy
While automakers like General Motors, Ford, and Rivian also received recognition, Tesla’s multiple awards stood out given the company’s outsized role in popularizing software-defined vehicles over the past decade. From frequent OTA updates to its data-driven approach to autonomy, Tesla has consistently treated vehicles as evolving software platforms rather than static products.
This has made Tesla’s vehicles very unique in their respective sectors, as they are arguably the only cars that objectively get better over time. This is especially true for vehicles that are loaded with the company’s Full Self-Driving system, which are getting progressively more intelligent and autonomous over time. The majority of Tesla’s updates to its vehicles are free as well, which is very much appreciated by customers worldwide.

