Autonomous cars are cars which drive themselves by perceiving the environment around them in real time. As the world makes strides in the Artificial Intelligence arena, autonomous vehicles seem to be one for the first big mainstream products born out of it. Most of the traditional car companies have deployed huge capital in this space. Tech giants like Google, Apple are also deep in this space. This article aims to provide a high level map of the future of transportation and set up the necessary terminologies for diving deeper into this domain.
We split autonomous technology into two main sections, vehicle based and network based.
Everything hardware and software based on the vehicle to help with driving falls under the vehicle section. When one car is on the road, multiple cars are learning even though they maybe off the road. This is because each car is a node in the network of cars. All technology relating to that comes under network based. Different aspects of each of the sections will benefit the other but it is worth segregating them to study them deeper.
Sensor Fusion: Each autonomous vehicle relies on its sensors for tracking objects over time. Sensor Fusion includes Sensor Network Infrastructure, LiDAR technology, Radar technology, Vision, Camera technology and image recognition.
Localization: It helps us determine the position of the vehicle with respect to the world. GPS is great but its accuracy is only within a few meters. Since human safety is at stake, we need centimeter level accuracy. Localization includes GPS and precision positioning.
Control: Ultimately, a self-driving vehicle is still a vehicle, and we need to send steering, throttle, and brake commands to move the car through the world. Control includes automation systems, data, platform and accident mitigation systems.
Fleet Automation: For every vehicle crash or new environment learning in regards to an autonomous vehicle the entire network benefits making transport safer. Fleet Automation includes full stack (hardware+software) automation, communication, ride hailing, prediction and vehicle management technology. This is the technology which will proactively send data to the network to benefit other nodes of the network as well as to help in automating the ride hailing and carpooling infrastructure.
State of Autonomous Transport — Key Trends
1. Funding by Category: ‘Control’ has more investment than ‘Sensor Fusion’ even though the number of startups are more in the latter.
2. Investment*: Investment has tripled since 2013 every two years. Funding is at its peak in 2017.
3. Most Funded Startups: Investment in Uber, Didi is higher than investment in all other companies combined.
4. Global Breakdown: San Francisco and Bay Area are still the best locations when it comes to autonomous vehicles.
7. Acquisitions: Fleet Automation has the maximum number of acquired startups.
· Product Strategy
I. Horizontal Product Expansion: Fleet Automation startups can expand their offering laterally by leveraging existing data and technology stack.
II. Distribution leverage: Ride Hailing startups are moving laterally as they posses a unique distribution leverage. Traditional car companies also hold a similar leverage.
· Competition & Market Evolution:
I. Acquisition: Startups on the lower side (Sensors, localization) can be acquired by startups on the upper side (Control, Fleet Automation) to improve their existing technology stack. Traditional car companies can also acquire startups on the upper side. (Example: GM acquisition of Cruise)
· Opportunities for Entrepreneurs:
I. Future Growth: As the market matures and technology deploys, technical entrepreneurs who can understand the legal labyrinth will have a unique advantage.
1. Funding data: Crunchbase, Techcrunch
2. Company info: Company websites
*Investment: Venture Funding, Public Market funding or parent entity funding.
Disclaimer: Data for this study is sourced from freely available sources. Sole motivation for this study is to increase personal understanding of the autonomous car space. We have omitted startups and traditional car companies if we did not have complete public data about location, funding or their involvement in the space. We aim to make this a living document, please share any thoughts and feedback and we will update this on a periodic basis. Feedback on any omissions, factual errors are also welcome.