Mercedes-Benz Vaults To Level 3 Autonomy As Sales Slow

Level 3, often referred to as "conditional automation," represents a stage in autonomous driving technology that closely aligns with our everyday understanding of self-driving vehicles. In Level 3 vehicles, drivers can essentially let go of the steering wheel under specific environmental conditions. However, it's crucial for the driver to remain alert and ready to take control if the autonomous system prompts them to do so.

In practical terms, Level 3 autonomy empowers a vehicle to perform a range of tasks such as accelerating, braking, steering, parking, obeying traffic signs and signals, merging onto and exiting from highways, and maintaining a safe following distance from other vehicles. Leveraging a sophisticated network of sensors, processors, and algorithms, Level 3 vehicles possess the capability to autonomously make decisions, effectively managing a substantial part of the driving experience. Nevertheless, certain situations, like intricate construction zones, accident scenes, or other exceptional scenarios, may necessitate driver intervention.

Mercedes-Benz Group AG (OTCPK:MBGAF) has dubbed its Level 3 system "Drive Pilot," and this groundbreaking technology is slated to make its U.S. debut in late 2023 as an optional feature on the EQS and S-class sedans, as announced September 27, 2023.

Chart 1

Mercedes-Benz has entered into a strategic partnership with NVIDIA to integrate artificial intelligence ("AI") and metaverse technologies into its design and development processes. NVIDIA DRIVE Hyperion is an end-to-end, modular reference architecture for designing autonomous vehicles (AVs).

According to the company, more than 50 automotive companies around the world have deployed over 800 autonomous test vehicles powered by the NVIDIA DRIVE Hyperion automotive compute architecture, which has recently achieved new safety milestones.

Central to this groundbreaking architecture is NVIDIA DRIVE Orin, a platform that delivers high-performance and energy-efficient AI computing power. It supports a comprehensive sensor suite and software, enabling enhanced assisted driving and, ultimately, level 3 conditionally automated driving in a safe manner.

With a remarkable capability to perform 254 trillion operations per second, DRIVE Orin provides ample computing capacity for continuous software enhancements. These enhancements can be seamlessly delivered throughout the vehicle's lifespan via over-the-air software updates, accessible through various channels such as mobile apps, web interfaces, or directly from within the car.

Furthermore, Mercedes-Benz is expediting the development of these systems by harnessing the high-fidelity NVIDIA DRIVE Sim platform, which is grounded in NVIDIA Omniverse. This cloud-native platform offers physically accurate and scalable simulation capabilities, allowing automakers to design and test autonomous vehicle systems in a wide array of rare and challenging scenarios.

Mercedes-Benz leverages a combination of advanced technologies and partnerships to power its vehicle systems, particularly for driver-assistance and autonomous driving features. By combining camera and radar inputs with the expertise and technology provided by LG, Bosch, and NVIDIA, Mercedes-Benz aims to enhance the perception, decision-making, and overall performance of its vehicle systems, ultimately contributing to the development of advanced driver-assistance and autonomous driving capabilities.

Competition from Alternative Automotive AI Chips

Autonomous driving systems require a lot of computing power because they have to process a variety of sensor data. Among them, the processing of visual image data from the camera consumes the most computing power.

For each level of autonomous driving, the required computing power increases by at least several times. For example: L2 level requires 10+ TOPS of computing power, L3 requires about 100 TOPS of computing power, L4 level may require about 500 TOPS of computing power, and L5 level even requires more than 1000+ TOPS of computing power.

In addition to theoretical hardware computing power, the actual computing power utilization is also crucial. The architectural design of different AI accelerators usually leads to different actual utilization of hardware computing power. Therefore, the same neural network model runs on two AI accelerators with the same hardware theoretical computing power. Different measured performances.

Most autonomous vehicles employ a system of cameras, radar, laser sensors and other technologies to assess road conditions and adjust driving behavior. These vehicles might have adaptive cruise control, traffic lane adjustments and automatic braking, steering and acceleration.

The level of autonomous driving and the computing power of the chip are inseparable. The autonomous driving industry generally believes that the chip computing power (Trillion Operations Per Second ("TOPS")) required for L2 autonomous driving is below 10 TOPS, L3 requires 30 to 60 TOPS, L4 requires more than 100 TOPS, and L5 requires more than 1000 TOPS. It is precisely because of this that chip computing power TOPS has become the core weight of various chip competitions.

If car companies use multiple chips to build autonomous driving domain controllers, they can reach a maximum of 1,024 TOPS, which can support L4 autonomous driving.

While a single chip's computing power TOPS is a key indicator, it's not the only one. Autonomous driving is a complex system that requires car-road-cloud-side collaboration. Therefore, in the competition of autonomous driving chips, in addition to the core, there is also software and hardware collaboration, as well as platforms and tool chains.

At present, there are numerous competitors in the automotive AI chip market, presenting headwinds to Intel and Mobileye, including Qualcomm (QCOM), Nvidia (NVDA), Tesla (TSLA) and Chinese companies Huawei, Horizon Robotics, Black Sesame Technologies, and Xin Chi Technology.

Table 1 shows a list of companies, their chip versions (current and planned), and TOPS for the chips.

NVIDIA Processor Roadmap

As noted above in Table 1, Nvidia's Xavier processor features a programmable CPU, GPU, and deep learning accelerators, delivering 30 TOPs. Nvidia's next-generation autopilot chip Orin chip will also start mass production in 2022. The Orin chip has a single computing power of 254 TOPS, which has exceeded EyeQ5 computing power by 10 times.

Nvidia's Thor processor is packed with high-power computing to support the idea of having a single chip to handle autonomous driving and cockpit functions. To put this into perspective, Thor can handle 2,000 TOPS, which is twice the speed of Atlan and eight times the speed of the current Orin processor.

Nvidia announced that it plans to launch SoC: Nvidia Drive Atlan in 2024, which will directly push the computing power of a single chip to 1000 TOPS. Nvidia's SoC roadmap is shown in Chart 2.

Chart 2

EV Growth

Chart 3 shows the growth of Electric Vehicles, the prime type of vehicle for ADAS. EVs are projected to grow from 10.3 million units in 2023 to 60.6 million in 2035, a 6X increase, according to The Information Network's report "Global and China EV Batteries and Materials: Technology, Trends and Market Forecasts."

Chart 3

Investor Takeaway

According to Mercedes 2023 guidance:

"With regional differences the overall growth momentum of the world economy is likely to remain rather subdued in the second half of the year. Despite an ongoing monthly decrease in the rate of inflation, inflation is expected to remain above average in many places, which is likely to result in continued restrictive monetary policies by major central banks. These developments are likely to continue to weigh on consumers and companies and weaken economic growth accordingly. Geopolitical imponderables remain another uncertainty factor. By contrast, energy prices are expected to remain at a significantly lower level than in the previous year for the rest of 2023 and also on average for the year as a whole. The noticeably improved situation in global supply chains should continue to benefit the development of the automotive markets in the second half of the year, although market demand is expected to remain subdued in important markets."

While guidance is lackluster, share price performance compared to TSLA and Volkswagen (OTCPK:VWAGY) has been a positive 1-year growth of 31.79%, as shown in Chart 4.

Chart 4

Seeking Alpha Quant Ratings are a Hold for the three companies, as shown in Chart 5. I agree on the Hold for Mercedes.

Chart 5

Tesla stands out as the most seasoned contender in the realm of semiconductor technology among automotive manufacturers. In recent years, Tesla has phased out numerous third-party semiconductor components in favor of in-house research and development. This shift is driven by Tesla's exacting performance standards, promising substantial potential in the field. This trajectory somewhat mirrors Apple's development of its own mobile phone chips.

Tesla's automotive powerhouses primarily consist of two integral components: the Media Control Unit (MCU) and the AutoPilot Computer ("AP"). These represent two quintessential computing chips embedded within Tesla vehicles. The MCU, or Media Control Unit, oversees in-car media control functionalities, while the AP, or AutoPilot Computer, serves as the core computational hub for autonomous driving technology.

Volkswagen has begun directly purchasing strategically important chips that it believes are in short supply globally from 10 manufacturers, including NXP Semiconductors (NXPI), Infineon Technologies (OTCQX:IFNNY), and Renesas Electronics (OTCPK:RNECF).

Disclosure: A Mercedes is my daily driver.

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