Edge Computing
Data volumes generated by AVs can reach staggering proportions, in some cases more than 1 GB per second. Toyota predicts that the volume of data exchanged between cars and the cloud could reach 10 exabytes per month by 2025, which is 10,000 times the current amount. However, the cloud infrastructure was not originally designed to process such massive quantities of data rapidly enough to support autonomous vehicles.43
Transferring a fraction of this data to a cloud-based server for analysis is impractical due to bandwidth constraints and latency issues. For example, a 1ms latency corresponds to a very short distance, and this precision is necessary to avoid collisions and ensure smooth driving. It has been also estimated that the transmission of data over a network would require a minimum of 150-200ms, which is a significant amount of time considering the car is in motion and real-time decisions regarding car control need to be made.
Edge computing solutions offer real-time data processing capabilities, thereby minimizing reliance on network connectivity for decision-making. This not only reduces the need for online connectivity but also enhances the accuracy of decision-making.
Edge computing involves handling, storing, and interpreting data close to where it is created. To achieve this, AVs often require integration of two distinct in-vehicle computing systems. The first computer undertakes the substantial task of processing copious amounts of sensory data and images collected through cameras and various sensors. Concurrently, the second computer analyzes the processed image data, swiftly making intelligent decisions for the vehicle's safe navigation.44,45 The proximity of data allows for immediate data processing, empowering devices or vehicles to respond to information without delay.
“A lot of the research community looks at fancy algorithms, but making it work in real time on the edge is something which is still very challenging, especially if you go for larger and neural networks.” - Alexander Wischnewski - Managing Director and Co-Founder
Increasing security with Edge computing
Security is a paramount concern in the AV ecosystem, and edge computing supports this aspect effectively. Edge computing enhances the reliability of AVs by reducing dependence on distant cloud networks. Even in the event of network issues, AVs can function effectively as critical processing occurs locally. Additionally, edge computing helps improve data security by reducing communication overhead and limiting exposure to potential data breaches during transmission to remote servers.
In the last three years, automakers have implemented various layers of protection and redundancy to safeguard against power, network, and compute failures. Autonomous vehicles are equipped with the capability to dynamically re-route and power network traffic, as well as decision-making processes, to ensure a safe stop. The integration of Internet of Vehicles (IoV) and edge computing into a comprehensive distributed edge architecture ensures reliability and availability, with data being rerouted through multiple pathways to maintain access to necessary information.
Hardware Advancements for Edge Computing in AVs
AVs rely on specialized hardware for edge computing tasks. While general-purpose CPUs and GPUs are commonly used, there's a growing need for dedicated AI accelerator chips. These chips are optimized for deep learning inference and are designed to strike a balance between power consumption, speed, accuracy, and cost. For example, in August 2023, Google announced the launch of the fifth generation of its tensor processing units (TPUs) for AI training and inferencing. In contrast to its predecessor, this iteration is provide a 2x enhancement in training performance efficiency for every dollar spent and a 2.5x improvement in inferencing performance efficiency per dollar.46,47
Among the others, Field-Programmable Gate Array and Application-Specific Integrated Circuit chips are gaining importance due to their ability to provide efficient and customized processing for specific AI models. For example, advanced processors, such as NVIDIA's Xavier and DRIVE platforms, have been widely adopted in AVs. These processors offer high computational power, energy efficiency, and support for AI and machine learning tasks.
In terms of power efficiency, a high power consumption of GPUs can impact the driving range and fuel efficiency. Thus, AI accelerators that offer high performance with minimal power consumption are in demand. For instance, GTI's LightSpeeur 2803S provides high power efficiency, achieving a rate of 24 TOPS/Watt by conducting all CNN processing within its internal memory, rather than relying on external DRAM. It can effectively classify 448×448 RGB image inputs at a rate exceeding 16.8 TOPS while consuming less than 700mW at its peak power usage, all while maintaining accuracy levels comparable to the VGG benchmark. Gyrfalcon's CNN-DSA accelerators possess reconfigurability, enabling support for CNN model coefficients of varying layer sizes and types.43
Companies Developing Edge Computing for AVs
Big players have been pioneering the transformation of how autonomous vehicles function and interact with their environment using edge computing.
NVIDIA
The NVIDIA DRIVE integrates high-performance GPUs with AI software tools, enabling AVs to process vast amounts of sensor data in real-time. By deploying powerful AI hardware on board, NVIDIA enables AVs to make split-second decisions independently, without relying on external cloud servers.51
NVIDIA's edge computing solutions are advantageous for autonomous vehicles, offering powerful processing with NVIDIA GPUs. These GPUs enable AVs to efficiently handle complex sensor data from cameras, LiDAR, and RADAR, reducing latency by processing data locally. This low latency enhances safety, as the DRIVE platform allows rapid analysis and response to potential road hazards, improving overall road safety for autonomous vehicles.
In 2021, Volvo Cars partnered with NVIDIA to utilize their DRIVE Orin™ technology for autonomous driving computers in their next-generation vehicles, building on their ongoing collaboration. In conjunction with in-house software development and advanced sensors, including LiDAR, steering, and braking systems, this technology aims to enhance safety, personalization, sustainability, and continuous improvement through over-the-air software updates for Volvo's intelligent vehicle fleet.
In addition, in 2021, Zoox introduced a specialized robotaxi designed for daily urban transportation needs, driven by NVIDIA DRIVE technology. This robotaxi is among the pioneers to offer bi-directional capabilities, marking a significant step forward in advancing intelligent urban mobility.52
Qualcomm Technologies
Qualcomm Technologies is positioned in the field of wireless communication technology as a significant contributor to edge computing solutions in the AV domain. Launched in 2020, the Qualcomm Snapdragon Ride Platform showcases the integration of AI processing with vehicular systems, enabling AVs to process data from various sensors, including cameras and LiDAR, at the edge. This platform equips AVs with the computational power required to analyze complex environments and make informed decisions instantaneously.44, 53
The Snapdragon Ride Platform, at its core, enables effective sensor fusion, seamlessly integrating data from various sensors. This fusion enhances the accuracy of AVs' perception systems and deepens their understanding of the surrounding environment, improving overall safety and performance. Notably, Qualcomm's solutions emphasize reliability, ensuring that AVs equipped with their edge computing technology can remain operational even in scenarios with limited or intermittent connectivity.
In 2022, Volkswagen's Cariad software division announced a partnership with Qualcomm to source system-on-chips (SoCs) from Qualcomm's Snapdragon Ride portfolio for their autonomous driving software. These SoCs are a crucial hardware component for Cariad's standardized and scalable computing platform, enabling autonomous driving up to Level 4 standards, a central part of Volkswagen Group's future strategy.54
Lanner
Lanner is currently engaged in multiple autonomous driving projects. Since 2022, Lanner has offered AI-powered edge computing platforms designed to enable both autonomous and intelligent driving.
Lanner's edge computing solutions cater to the initial pre-processing stage of data collected by autonomous vehicles. Equipped with video cameras and an array of sensors such as ultrasonic, LiDAR, and RADAR systems, AVs rely on quick and efficient data aggregation and compression. Lanner's in-vehicle computers are equipped with multiple I/O ports that facilitate the seamless reception and transmission of data, thus expediting data processing.
Real-time Operating Systems for Autonomous Vehicles
A Real-Time Operating System (RTOS) represents a specialized operating system (OS) adept at orchestrating hardware resources and operations. It manages a spectrum of activities simultaneously and within established time boundaries. These tasks range from coordinating application program scheduling and writing data onto storage disks to transmitting information across networks.
In AVs, RTOS systems are used for sensor fusion, control systems, safety-critical functions, and real-time communication within the vehicle and with external infrastructure. Moreover, they enable redundancy and fail-safe mechanisms, real-time mapping and localization, and secure over-the-air updates to keep the vehicle's software current and secure. RTOS also provides hardware abstraction, making it easier for developers to create software that can run on various hardware platforms. In this way, companies utilize RTOS to deliver the precision, low latency, and reliability required for the complex task of autonomous driving, ensuring the safe and effective functioning of these vehicles on the road. 55
Advancements in RTOS Systems for AVs
Specific types that have garnered significance in the context of AVs. The technologies presented below have emerged as crucial factors in AV development due to their inherent reliability, ability to enhance performance, and robust developer support.
Autoware Foundation
Autoware, an open-source project, aims to provide a comprehensive software stack for self-driving technology. It utilizes ROS and various RTOS components to facilitate AV development.
Automotive Open System Architecture (AUTOSAR)
AUTOSAR serves as an software development standard for Automotive RTOS and electronic control units (ECUs). Industry players such as KPIT Technologies, RTA-OSEK (from ETAS, a part of Bosch), and Elektrobit utilize AUTOSAR to facilitate the harmonization of automotive software, enhancing interoperability across various components. It supports the integration of different RTOSes and middleware components, promoting interoperability among various AVs.
Noteworthy AUTOSAR trends in 2023 encompass a heightened emphasis on cybersecurity, empowering secure vehicle-to-everything (V2X) communication, bolstering support for over-the-air (OTA) updates, and seamless integration of artificial intelligence to advance driver assistance systems (ADAS) and autonomous driving capabilities.5656
Automotive Grade Linux (AGL)
As a specialized version of the Linux Open-Source operating system, AGL is tailored specifically for automotive applications, offering high-standard RTOS capabilities. With the participation of ten automotive brands and 140 subsystem suppliers, the AGL project strives to provide a versatile platform for building innovative and connected automotive systems.
Connected Vehicle System Systems Alliance (COVESA, formerly GENIVI)
COVESA focuses on developing reference approaches for automotive systems, including RTOS solutions. Its efforts include ensuring compatibility and coexistence with AUTOSAR-based systems, and promoting seamless integration across the automotive software landscape.
BlackBerry QNX Automotive
BlackBerry QNX Automotive stands out for its explicit design to cater to embedded automotive systems. Prioritizing speed, reliability, and security, it has found deployment in over 235 million vehicles globally. Its versatility spans various automotive ECUs, ranging from telematics and infotainment to advanced driver assistance systems (ADAS) and safety features. It offers a microkernel architecture, making it suitable for safety-critical applications. It is used in various AV platforms for functions like sensor fusion, control, and communication.
BlackBerry QNX takes valuable insights from its AVIC system and uses them to help automotive manufacturers, suppliers, SMEs, schools, and research groups achieve ISO 26262 safety certification for their production systems. Strategy Analytics, an independent research firm, has reported that more than 215 million vehicles worldwide utilize BlackBerry's QNX software in 2022.
This marks a 20 million increase from the previous year. Automakers utilize BlackBerry QNX software for a variety of applications in today’s interconnected vehicles, such as digital dashboards, advanced driver-assistance systems, instrument panels, sound systems, and entertainment systems. Companies like BMW, Bosch, Continental, Honda, Mercedes-Benz, Toyota, and Visteon have incorporated this embedded software in their vehicles.
VxWorks
xWorks is a renowned and extensively adopted commercial Real-Time Operating System (RTOS) that delivers consistent and rapid response times. This high-performance RTOS boasts over three decades of industry experience and more than 2 billion installations across a diverse range of embedded systems worldwide. One of the notable aspects of VxWorks is its classification as a "Hard RTOS."
This distinction places it among the elite RTOS solutions favored in AVs. Hard real-time capabilities ensure that it meets stringent timing requirements, making it suitable for AVs where precise and deterministic responses are imperative. The manufacturer also has a proven history of providing fault-tolerant operation, essential in environments where AV failures can have severe consequences. Finally, the adaptability of the technical solutions ensures that VxWorks can be integrated into a wide array of vehicle applications, from simple embedded control systems to complex tasks like autonomous decision-making.
Green Hills Software
This company specializes in safety-critical software solutions, including RTOSes. Its INTEGRITY RTOS is used in AVs to enable real-time processing and secure partitioning of tasks. Since the beginning of 2023, Infineon has been working together with Green Hills Software to provide extensive and reliable safety and security solutions tailored for the TRAVEO T2G MCU families in the automotive sector.
This software solution has been fully tested, creating a comprehensive package suited for various automotive uses, including electrification, managing vehicle body controls, gateway functions, and infotainment. This partnership provides car manufacturers with a ready-to-use, integrated solution. It is designed to be efficient in its use of memory, yet it does not compromise on quality, performance, or reliability.
Apex.AI
Apex.AI is dedicated to improving its RTOS known as Apex.OS, which was launched in 2020. Engineered for exceptional scalability and adaptability, Apex.OS is meticulously crafted to serve a diverse spectrum of applications in the domain of AVs.57 This system includes an easy-to-use software development kit (SDK) to improve advanced automotive software creation.
This product functions as a meta-operating system, enabling the rapid and safe development of complex applications, significantly faster than traditional methods. It offers a comprehensive collection of ready-to-use SDKs and tools to assist in application development, debugging, and testing.
When implemented, the operating system facilitates a smooth transition from software prototyping to production in the automotive industry, saving time and resources for users. Apex.OS aims to enable the transition from hardware-centric to software-centric vehicles, providing a comprehensive operating system to develop mobility applications in an optimal manner. Apex.AI aspires to be the Android of the automotive world, striving to become the dominant operating system in the industry.
Cognata
Cognata is a specialized company in developing simulation and testing solutions tailored for AVs. The RTOS developed by Cognata is purpose-built for integration within simulation and testing settings, and it further holds applicability in the operational landscape of production AVs.58 Cognata has introduced a new service utilizing Microsoft Azure, enabling automotive companies to virtually test ADAS/AV sensors in realistic simulation settings.
This platform offers a wide range of ADAS/AV sensors and robust simulation tools, facilitating quick and comprehensive analysis of sensor placement and capabilities on vehicles. Users can conduct tests in various environments, including urban, highway, and off-road settings, at different times of the day and under diverse weather conditions. This new product runs on Microsoft Azure and uses AMD processors and GPUs.
It aims to help automotive customers efficiently assess ADAS/AV sensors through authentic simulation environments. Cognata’s automated driving perception Hub addresses this by offering a photorealistic environment for testing various sensor models and custom presets across terrains, times of the day, and weather conditions. This significantly reduces the time needed for sensor evaluation.
Cognata’s collaboration with Microsoft accelerates the digital transformation of automakers using Azure’s global cloud, services, and computing capabilities, thereby fast-tracking the development, verification, and validation of ADAS/AV features.
Leadership Interviews
Interview with Mouser
Interview with Murata
Interview with MacroFab
Interview with Nexperia
Interview with SAE International
Interview with Autoware Foundation
Interview with NVIDIA
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