Augmented Judgment – Autonomous Vehicle


Autonomous vehicle industry and the problem it solves

The global autonomous vehicle market is estimated to be $42B by 2025. This is segmented by Partially Autonomous Vehicles and Fully Autonomous Vehicles, representing $36B and $6B, respectively. The main benefit from autonomous vehicles is the expected increase in safety. Thousands of people die in car accidents and autonomous vehicles are expected to be less error-prone than humans. Autonomous vehicles also have additional benefits such as helping those with physical limitations to mobilize easier. Other benefits include: reducing the number of vehicles on the road, lowering the amount of traffic violations, and providing a more comfortable and efficient way of transportation. These solutions can then be expanded to optimize ride-sharing services and reduce traffic congestion and lower fuel consumption.

Optimus Ride is at the forefront of creating an entirely autonomous vehicle. It leverages a system of hardware products (and machine learning algorithms) in concert with human drivers to develop semi-autonomous vehicles. Initial use cases of the vehicles include shuttle services in communities, commercial developments, airports, college campuses, amusement parks, and other relatively low traffic areas.

The solution is two-fold – hardware augmenting people becomes software augmenting people.

Vehicles come with two lightweight Velodyne lidar sensors, eight cameras, GPUs and motion sensors, and a proprietary switchboard that translates the sensor data into mechanical responses from the vehicle. The system uses cameras and lidar sensors, but dependence on lidar will decrease as Optimus’ accumulates data, which will train computer vision algorithms. The system will eventually shift reliance to computer vision, which will use less expensive hardware and is more scalable.

Path to autonomous vehicle

Time to prepare, calibrate, test, and deploy a vehicle currently takes several weeks. That timeframe is expected to decrease as the company solidifies formal production partnerships with OEMs. Discussions are ongoing for several pilots, including some in Massachusetts and Florida. The product is currently comprised of a full-stack autonomous solution encompassing lidar-based perception (front and rear), vision-based perception (via front and rear cameras), motion planning (via wheel encoders), computer integration (via the NvidiaDrive PX platform), and drive-by-wire control. Lidar is the industry standard, but Optimus is working towards an advanced computer vision-based autonomous solution through a multi-layered rendering from three distinct visual input techniques: visual slam, deep learning, and stereo vision. The resultant camera-focused autonomous system, complete with sensor fusion, will represent the core IP and proprietary software. The unique solution will have the capability to detect objects and obstacles in the vehicle’s path and determine the location of the vehicle in proximity to its surroundings with centimeter-level accuracy. With this scalable turnkey solution available, Optimus could deploy full fleets of autonomous vehicles controlled by client platforms.

Roadmap to vision-based autonomy                Vehicle use cases


The fully autonomous vehicle landscape is highly competitive:

Robust proprietary data is a key competitive differentiator in the autonomous vehicle space. Driving data will train machine learning algorithms, underpinning self-driving technology. To scale, video or image-based data will be the most valuable because it can lessen dependence on expensive lidar technology by shifting reliance to computer vision and software. Incumbent players have accumulated road-mileage but capturing vision-based data remains to be an arms race.

Competitive Landscape

  • Automakers (Ford, GM, Tesla) have been actively establishing partnerships with technology startups and making strategic acquisitions and investments in the autonomous vehicle space.
  • Ride Hailing Companies (Uber, Lyft) are well-aware of the transition to self-driving cars and are developing in-house, through partnerships or via acquisitions.
  • Technology Companies (Google, Apple)–Alphabet leverages GPS, Waze, and Google Maps to generate routes for autonomous vehicles and is developing autonomous offerings via Waymo.
  • Autonomous Software Startups (Nexar, Nauto,, nuTonomy, Varden Labs, Aurora, NextEV)–have received significant funding to power the autonomous shift

But Optimus Ride benefits from a proprietary dataset and pilot partnerships:

Over time, Optimus Ride can leverage its multifunctional ride complex and license to operate in Boston’s self-driving vehicle zone, along with partnerships it has signed with private developers and community and transit authorities to use controlled city zones to accumulate driving data. One of the most obvious constraining market factors is the limited pool of talent, which is a crucial factor driving the numerous strategic acquisitions by incumbents that have defined the autonomous vehicle market in the last year. The technical expertise of the founding team provides a strong competitive advantage.

Team Members: Brentt Baltimore, Moises Numa, Corey Ritter, and Mitchell Stubbs


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