“Long haul redefined”

Founded: 2015, Beijing,  China.

Category: Artificial Intelligence, Autonomous Vehicles, Transportation

Primary office:San Diego, California, USA

Core technical team:

San Diego, USA; Beijing, China

Status: Private

Employees: 251-500

Amount raised:USD$298.1million(8 rounds – Sept 2020


  • TuSimple leverages computer vision and AI decision making technology to address cost, safety, and supply shortage challenges related to human labor in the two largest long-haul trucking markets in the world, the US and China
  • The company offers a full stack solution for autonomous vehicle (AV) operations in the trucking industry, specifically for warehouse-to-warehouse long haul operations. The company has developed proprietary solutions for sensors, vehicle integration, and fleet operations, and sells to upstream component manufacturers and downstream shippers and truck fleet operators.
  • Early investments originally went into research and development efforts, technology co-creation partnerships, real-world data acquisition and testing on designated public roads. Recent investments have gone into what they call an autonomous fleet network (AFN), fleet growth, and a large emphasis on refining the autonomous technology to the point where humans no longer need to ride in the trucks.
  • The distinct competencies of the company are its ability to use high definition (HD) cameras to extend the AI’s ability to perceive traffic changes up to 1,000 meters ahead, its ability to integrate autonomous capabilities into existing trucking technology, and its ability to continuously improve the safety and efficiency of its autonomous driving algorithms.
  • Origin and evolution: Founded by three Chinese computer scientists (two PhDs and a bachelor’s degree). The President and CTO, Xiaodi Hou, has a PhD in computation and neural systems from California Institute of Technology is credited as being the main founder
  • TuSimple started out developing car-identification software that identifies the make and model of cars after analyzing images. Seeing greater business potential for the visual-recognition technology in autonomous driving applications, they decided to move into haulage rather than selling the technology to fleet owners or logistics firms
  • With an angel investment of capital of 50,000,000 yuan (~$8+ million USD) the company established R&D facilities in San Diego and in Beijing, and then later made a name for itself internationally after it set 10 world records in 2016 for their algorithms’ performance on world benchmark tests for autonomous driving.
  • In 2016, the company set about demonstrating its proof of concept level 4 automation capabilities on a test drive from San Diego, CA to Yuma, AZ, and later raised $20m USD in a Series B round
  • In 2017, the company demonstrated its SAE Level 4 self-driving system in Shanghai, China and its capability to ramp up deployment efforts in the US, starting in Tucson, Arizona, resulting in a Series C funding round of $55m USD
  • The company shifted its focus from R&D to commercial operations in 2019, having established partnerships with tech suppliers and downstream customers. They were earning money and validating their autonomous system and raised another $95 million
  • As of 2020, the company is targeting an initial public offering (IPO) in the near future that will fund the expansion of a nationwide commercial fleet of AV long-haul trucks in the US.


  • Company valuation: USD$1+ billion (2020)
  • Estimated Revenue: N/A


  • 2015 – Raised an angel round of CN¥50,000,000 (estimated USD$8.06 million) from 7 investors upon founding
  • 2016 – sets 10 world records and placed first KITTI and Cityscapes benchmark tests, the world-leading benchmark for vision based autonomous driving. Also won by a landslide facial recognition benchmark tests 300W and AFLW
  • CTO Xiaodi Hou developed spectral saliency theory that was the most influential research in the field of visual attention mechanisms from the past 10 years (2016 perspective)
  • Principal Scientist Naiyan Wang was the first person in the world to apply deep learning to object tracking fields
  • Unicorn status in February 2019 – 4 years after founding
  • Facilities in San Diego, Tucson, Shanghai and Beijing
  • Credited with world’s first autonomous freight network


  • TuSimple Connect – software that acts as driver for entire fleet of AV trucks. Responsible for the driving behavior of vehicles and assumes responsibility for operations and accidents. Unclear whether subscription based, per trip or per mile.
  • Shipping services – shippers (consignors) pay for autonomous trucks to haul trailers of goods from depot to depot
  • Licensing – technology IP licensed to original equipment manufacturers (OEMs) and other Tier 1 suppliers that want to build the trucks and the components that go in them


  • Partnerships
    • Significant emphasis on partnerships with upstream component suppliers and downstream shipping services.
  • Direct
    • Website for in-bound sales.
    • Outbound sales must be done directly or through network connections.
    • B2B clients are large and highly visible in marketplace.


  • Big data, cloud, AI/ML
    • Deep knowledge on computer vision perception and applying neural networks to computer vision
    • Predictive decision making for traffic and actions to take
  • Technology
    • Retrofitting existing trucks (e.g., Peterbilt) to integrate autonomous vehicle capabilities
    • Co-creation of purpose-built components with OEMs and technology suppliers (e.g., high definition (HD) cameras, drive trains, braking systems, steering systems, cloud computing hardware and software, etc.)
    • Ability to tap into, collaborate with and leverage technology research centers in both China and the US
  • Brand
    • Positioned itself as a provider of increased safety (for roads) and stability (for supply chains
  • Business model
    • Aligned to pursue fastest path to commercial application of technology and funding that will support rapid ecosystem level expansion
  • Ecosystem
    • Attract investors that bring distinct technological competencies or strategic value chain positioning
    • Developed an enabling position by strengthening incumbents rather than trying to displace them

Distinct AI Features


  • Neural networks applied to computer vision
  • Asserting AI is based on convolutional neural network technology.

  AI use

  • Computer vision (object detection) – advancing technology
  • Support service providers
    • AI drives trucks that transport goods – enables trucks to drive longer than humans can, decreasing number of hours for long-haul trips (e.g., 5-day trip can be done in 2 days)
    • AI tracks, coordinates and finds efficiencies for customer fleets – reduces time, labor costs, risks of labor shortage, gas used and wear on breaks (AI prediction optimizes use of throttle and brakes less)
  • Enable resource integration between service providers and beneficiaries
    • Assumed to coordinate network more efficiently given high-degree of automation – not specifically stated
  • Support beneficiaries’ well-being (e.g., end-customers)
    • Goods travel long distances in shorter time at reduced cost
    • Roads are safer for drivers – risk of driver fatigue eliminated and AI decision making response time 15x faster than human’s

AI useRate of return on customer’s investment to make AI work


  • OEMs and component suppliers – Reduced time to access specialized IP required to design new trucks to meet AV needs
  • Shippers and Fleet owners – Increased awareness of what new technology will mean for industry
  • Shippers and Fleet owners – small increase in capacity

Long term:

  • Shippers and Fleet owners – addresses significant risk in capacity due to growing labor shortage in drivers
  • Shippers and Fleet owners – increased profitability by decreasing cost of labor (40% of revenue in US) and other variable costs (optimized driving)
  • Shippers and Fleet owners – decreases time to arrival for goods that require shipping distances of over 8 hours of driving
  • Truck drivers – less time out of town, less risk of driver’s fatigue and accidents


  • Initially used data acquired from mounting cameras on existing trucking companies’ trucks
  • Continuously increases proprietary database of data captured on their own trucks

Quantum Computing

  • N/A



  • Proprietary computer vision perception algorithms, driving algorithms, and traffic prediction algorithms
  • Purpose built technology co-created with Tier 1 suppliers
  • Proprietary designs for truck components specifically made for AVs
  • Partnerships with large tech and service incumbents such as Nvidia, UPS, US Postal Service, Peterbilt, Mando Corp, Penske, US Xpress, etc., which increase their access to advanced technologies, new tech developed with their specifications, complementary capabilities and future customers
  • Strategic cooperation agreement with the Caofeidian District of Tangshan in the Hebei Province of northeastern China (near Beijing) that allowed TuSimple to test autonomous trucks on public roads and create a zone to commercially operate an automated logistics zone
  • Regulatory permission from US state and federal governments
  • Highly technical founding team and research partners (university research centers)


  • Train driving decision-making algorithm using deep learning on a combination of multiple computer vision sensor inputs (LiDAR, radar, and high definition cameras)
  • Retrofit trucks to be autonomous
  • Partnership acquisition
  • Internal coordination to manage co-creation projects with world leading technology companies
  • Fundraising
  • Government regulatory liaising
  • 3D mapping of shipping routes


  • Build a camera-centric perception solution as the core technology instead of LiDAR centric
    • Use deep learning super-computer plus low-cost vision and radar sensors to make commercialization
    • easier, and then integrate LiDAR at a later stage
  • Build team with deep technology expertise from both China and the USA by establishing R&D
    facilities close to talent (in Beijing and San Diego) (60% of early team were PhDs)
  • Train computer vision enabled deep learning with data collected aboard manually driven trucks
  • Focus on a production level system: find the fastest path to commercial application
  • Focus on mastering highway driving scenarios for faster commercialization without human
  • Build out fleets in both US and China by first retrofitting trucks to be autonomous and then have
    partner companies build the trucks they require to operate mature business model (one brain, many
  • Raise money between each major milestone demonstrated to market
  • Acquire strategic investors that are upstream and downstream in the value chain
  • Partner with original equipment manufacturers (OEMs), Tier 1 suppliers, and sensor technology
    companies to access production-level quality components that are designed to integrate into the
    autonomous system solution (e.g., powertrain, braking and steering systems)
  • Take an ecosystem approach by partnering with shippers, technology companies and service providers to
    expand service coverage

    • Phase 1: Two cities in Arizona and four cities in Texas
    • Phase 2: Integrate LA and all major cities in the South of continental USA (South of Nashville)
    • Phase 3: Rest of USA
  • Complete an IPO to raise at least a USD$ 1 billion to fuel expansion of US AFN