The concept of autonomous vehicles is growing significantly due to the exponential increase in ride-hailing services. Sensors are the critical components of self-driving vehicles. Radars, lidars, cameras, and HD maps enable an autonomous vehicle (AV) to conceptualize and visualize its surroundings and detect objects on its way.
These sensors have three essential tasks to perform, localization, detection, and tracking, and all these tasks are achieved through data fusion performed by processors at different levels. Autonomous vehicle processors are developed by various semiconductor chip manufactures and various heavily funded start-ups. Companies are focusing on the up-gradation of their processing kits for new high-level autonomous vehicles in the market.
Recently, NIO, a Chinese automobile manufacturer, has partnered with Qualcomm and NVIDIA to develop the next generation of automated vehicles. NIO plans to harness the power of Nvidia's Drive Orin system-on-a-chip (SoC) for the next roll-out of electric vehicles to be launched in 2022. The idea behind this collaboration is to develop smarter automated driving designs.
In another development, Tesla plans to partner with Samsung for a 5-nanometer for autonomous vehicle processors, based on the report shared by Asia-E in Korea. Earlier, Tesla planned to use a 7-nanometer process by TSMC, a Taiwan-based semiconductor company.
According to the market intelligence published by BIS Research, the global autonomous vehicle processor market was estimated at $5.07 billion in 2019 and is projected to grow at a CAGR of 22.98% during the forecast period, 2020-2030. The growth of the market is attributed to the factors including increasing push from the government to develop connected and autonomous infrastructure and the accelerating demand for new autonomous vehicle processors due to transportation as a service.
However, there are certain challenges that are hindering the growth of the market, such as high-cost structure of autonomous vehicles and their processors, lack of advanced infrastructure in developing countries, lack of proper protection of autonomous vehicles from hackers, and the severe impact of the global pandemic on the overall supply chain of the automotive industry.
The applications of the autonomous vehicle processor market are based on the level of autonomy of the vehicles, including Level 2 autonomous vehicle (AV), Level 3 autonomous vehicle, Level 4 autonomous vehicle, and Level 5 autonomous vehicle. Level 2 AVs have been in the automotive ecosystem for a couple of years and are mature enough to be upgraded to the next level.
Level 2 autonomous vehicles generally require less powerful processing units for advanced driver assistance systems (ADAS). NXP Semiconductors, Mobileye, and Renesas Electronics Corporation are some of the leading autonomous processor manufacturers, working on Level 2 AV processors. Some of the autonomous vehicle manufacturers are working on Level 2 autonomy including Tesla Autopilot and Cadillac (General Motors) Super Cruise systems.
In Level 3 autonomous vehicles, the jump of technological perspective is substantial. Level 3 vehicles have “environmental detection” capabilities and can make informed decisions for themselves, such as accelerating past a slow-moving vehicle. However, they still require a human driver.
Level 4 autonomous vehicles can drive themselves and are capable of controlling steering, braking, start/stop, and acceleration/deceleration, among others, all at once. Level 4 autonomy has been launched by self-driving "robotaxi" operators only. However, the taxis require a human driver on board to take over in case of emergencies.
Level 5 robotaxis are fully automated, completely self-driven, and do not require any human intervention for the steering wheel, pedals, and joysticks. A fully automated driving system enables the vehicle to operate in all driving modes and under any circumstances that can be handled by the human driver. It is anticipated that using Level 5 technology in automobiles can reduce fatal road accidents by 90%.
Further, it is expected that the adoption of autonomous vehicles can reduce the traffic density due to fleet formation by connected vehicles utilizing the road network. Also, autonomous systems enable these vehicles to park themselves, reducing the congestion further.