The path towards achieving fully autonomous vehicles is a lengthy and intricate journey. Systems that incorporate cutting-edge technologies to enhance vehicle autonomy levels must undergo rigorous safety and durability testing before they can be integrated into vehicles meant for public roads. These systems, collectively referred to as Advanced Driver Assistance Systems (ADAS), encompass a complex network of power supplies, sensors, and electronics. The effectiveness of ADAS largely hinges on the precision of the sensing equipment and the speed and accuracy of the onboard autonomous controller's analysis.

Artificial intelligence (AI) plays a pivotal role in the functioning of autonomous vehicles, particularly in the context of onboard analysis. Market research firm IDTechEx's recent report on AI hardware at the network edge predicts substantial growth, with AI chips – specialized semiconductor components designed to efficiently handle machine learning tasks – projected to generate over $22 billion in revenue by 2034. Among various industry verticals, the automotive sector is anticipated to experience the most significant growth, with a compound annual growth rate (CAGR) of 13% over the next decade.

AI chips in automotive vehicles are typically situated within centrally located microcontrollers (MCUs), which are connected to sensors and antennae to form a functional ADAS. These onboard AI computing capabilities serve various purposes, including driver monitoring (for driver-specific adjustments, monitoring drowsiness, and responding to accidents), driver assistance (for object detection and steering/braking corrections), and in-vehicle entertainment (with onboard virtual assistants akin to those on smartphones and smart appliances).

Of these functions, driver assistance is the most critical, as it directly influences the level of autonomous driving a vehicle can achieve. The automotive industry's reference point for defining different levels of driving automation is the SAE Levels of Driving Automation, ranging from Level 0 (no automation) to Level 5 (full automation). Presently, the highest state of autonomy for private vehicles is SAE Level 2, with the transition to Level 3 representing a significant technological leap.

A variety of sensors, including LiDAR and vision sensors, installed in the vehicle collect crucial data, which is then processed by the central computing unit for steering and braking adjustments. Effective processing relies on extensive training of the machine learning algorithms employed by the AI chips. This training involves exposing the algorithms to large volumes of ADAS sensor data, enabling them to accurately detect, identify, and differentiate objects, as well as gauge depth of field and distinguish objects from their backgrounds. ADAS functions can be passive (alerting the driver through sounds, lights, or feedback) or active (making real-time adjustments for the driver), necessitating swift and precise calculations.

The development of System-on-Chips (SoCs) for vehicular autonomy is a relatively recent phenomenon. Still, it's evident that there is a trend toward smaller node processes, which enhance performance. As autonomy levels rise, more computational power is required, and this shift to smaller nodes aligns with this demand, effectively outsourcing the computational complexity to semiconductor circuitry.

However, transitioning to smaller nodes entails higher manufacturing costs, particularly with the use of advanced lithography machines. This cost factor poses a significant barrier to entry for many semiconductor manufacturers. Consequently, several Integrated Device Manufacturers (IDMs) are outsourcing high-performance chip production to foundries capable of advanced fabrication.

To ensure cost efficiency in the future, chip designers must consider scalability in their systems. As the adoption of autonomous driving levels progresses incrementally, designers who overlook scalability may incur escalating costs in increasingly advanced nodes. Hardware that can adapt to more advanced AI algorithms is essential.

While it will take some time before we witness vehicles with the highest levels of automation on the roads, the technology to reach that point is gaining momentum. The next few years are particularly crucial for the automotive industry as it navigates the path toward autonomous driving.