What’s happening now?

To clearly understand what is happening now, we analyzed over 270 startups that are working on fully autonomous vehicles, full-stack self-driving solutions, and components (like sensors, mapping, simulation, computer vision) and more.

Autonomous driving market map

Autonomous Driving Systems

These are startups that are building a full-stack autonomous driving system like hardware and software (computer vision and sensor fusion software). They partner with automakers to deploy this tech, and in some cases, they retrofit existing vehicles as well.

AImotive, for example, offers software for cars and flights, and recently they partnered with NextChip to and announced aiWare, next-gen image hardware for self-driving vehicles. Drive.ai is another startup that partnered with Lyft to bring self-driving taxis.

China has unicorns in the autonomous driving industry. Pony.ai partnered with Guangzhou Automobile Group, China’s second-largest automaker, to bring autonomous driving fleets in Guangzhou. Momenta, partnered with the government of Suzhou to test and build a large-scale smart transportation system.

Complete Autonomous Vehicles

Companies like Faraday Future, Zoox, and Nuro are building AVs from scratch, and these vehicles are entirely different from the traditional cars on the road. These AVs don’t have steering wheels or dashboards. So, these cars are legally allowed to drive on public roads.

Nuro’s AVs are designed to carry cargo instead of people, and they target last-mile delivery. Faraday Future, on the other hand, develops fully autonomous electric vehicles with unique ownership models, in-vehicle content, and more. May Mobility is a Michigan based startup that develops AVs from scratch with a focus on system-level safety.

A self-driving shuttle from May Mobility. Source: Twitter

Mapping

Autonomous driving relies heavily on maps as they compare their surroundings to a digital map stored in their memory like humans. These maps are not google maps. These are HD maps with road-based information like lane sizes, crosswalks, and road signs. HD maps are built with data collected from sensors and software that turn them into a digital map.

Wayz.ai, a china based startup with US$80M in funding, offers HD maps, real-time location-based on cameras and sensors, safety testing, and cloud-based solutions for autonomous driving vehicles.

DeepMap is another company that develops map building software and licenses out to automakers and tech companies looking to teach vehicles how to drive. DeepMap got US$92M in Funding from Goldman Sachs, Bosch, NVIDIA, Accel, and A16z.

DeepMap’s HD maps | Source

SLAMcore is another startup that develops an advanced algorithm to help AVs, drones, and other systems to simultaneously map the surroundings and position themselves within it.

Civil Maps is using AI to convert raw sensor data into meaningful maps.

When it comes to corporates, Apple acquired Coherent Navigation for its self-driving cars. Google is building its own HD mapping with Waymo, and Volvo has left TomTom for Google.

Salesforce acquired MapAnything, a startup that offers mapping, schedule planning, route optimization, real-time geo-location, territory management, and geo-analytics. Audi, Daimler, and BMW acquired HERE Maps. Baidu is building out its own self-driving platform called Apollo, and it is planning to monetize maps, and the largest company in China believes that HD maps will be larger than their current business.

LIDAR

LIDAR or Light detection and ranging use Infrared sensors to determine an object’s distance and sensors pulse at a rapid rate to measure the distance. Traditional LIDAR tech is expensive and uses a 360 spinning camera to capture the surroundings. So, startups in this space are trying to reduce the cost of lidar sensors while maintaining high accuracy.

Bajara, for example, built a spectrum-scan lidar for autonomous vehicles that uses prism-like optics and shifting wavelengths of light.

Innoviz uses solid-state lidar tech has partnered with BMW and Magna to get their scanners in the market. Velodyne has a relatively expensive lidar tech which houses 128 lasers. LeddarTech has a patented solid-state lidar and has partnered with Acal BFI to bring their lidar to the European market. Aeva claims to have a lidar team that has a range of 200 meters and shoots a continuous lightwave instead of individual pulses.

Oculii develops military grade 4D sensors and uses advanced sensor fusion techniques to manufacture smarter, high precision sensors and systems.

Camera and Computer Vision

The camera, along with ADAS, can spot road signs, traffic lights, and street markings, but it is not that great with depth perception and distance measurement. Elon Musk believes that you can do a lot more with a camera and that you don’t need an expensive lidar tech. So, cameras capture highly accurate images, and computer vision software detects objects, signals, lanes, and asses the appropriate traffic signs and rules.

Light, for example, builds cameras with 16 lenses to extract highly accurate 3D images for self-driving cars. SenseTime is another company that offers computer vision and AI for AVs, and NetraDyne is developing deep learning solutions and vision-based analytics.

DeepScale is developing advanced neural networks for computer vision. Meanwhile, Prophesee has built an event-based computer vision tech that mimics how the human brain processes images.

Radar

Autonomous driving needs radar to detect an oncoming object’s distance, range, and velocity. Radar doesn’t have spinning parts. So, it is more accurate than lidar, and costs are meager compared to lidar.

Echodyne, a Bill Gates funded company is combining radar with computer vision-like software to create 4D imaging for AVs.

Zendar develops high-definition radar for autonomous vehicles to navigate in bad weather. Lunewave is a startup that 3D prints antennas with greater range and accuracy. Metawave uses metamaterials like Echodyne for longer detection range.

V2X

Sensors can’t detect objects outside their line of sight. So, a new class of automotive sensors will allow vehicles to see what’s beyond the sight. As long as cars are connected to the same network, it can detect other vehicles, pedestrians, and traffic signals.

Autotalks is a semiconductor company developing VLSI solutions for V2X and V2I communication. Valerann has developed an IoT system to sense the traffic environment. Meanwhile, Peloton provides and manages tools for saving fuel, avoiding accidents, and improving operational insight through the use of connectivity, automation, and data analytics.

Data and Simulation

Cognata’s real-world simulation for self-driving cars | Source

Autonomous Vehicles need to drive billions of kilometers to train the algorithms that guide the vehicle. This distance would take years. So, AV developers are amassing additional kilometers through simulation. With AI, startups are generating or augmenting existing datasets to train AVs, and this tech is useful in training AVs on dangerous and less frequent situations.

Startups like Cognata have developed a 3D simulation platform to provide customers with various testing scenarios. Parallel Domain, Righthook, and Metamoto are some of the exciting startups that help customers with simulation.

Now, what’s happening in with the autonomous driving industry, let’s look at what will happen next.