the AV Ecosystem Explained

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Automated vehicle systems are a complex endeavor for OEMs and suppliers who are pressured to develop solutions and strategy. Furthermore, there are literally hundreds of companies and thousands of products competing for a slice of the AV ecosystem. 

VSI’s Eco-Systems Infographic represents in-vehicle technologies used in the development and build of AV systems.  It is important to point out that this infographic only covers the in-vehicle technologies and does not include infrastructure, cloud, or enterprise level technologies even though they are an important part of the greater mobility eco-system.

The companies within the categories represented in this Infographic are known companies with known products. Most of the companies here have been vetted through an interview and/or product examination.

The top three layers are AV builds and platforms. 

AV Eco-System Map.png

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OEM Layer

The top layer are common OEMs that are actively developing AVs and most are still in the development stages. Some OEMs are further ahead than others in their deployment as there are now about six OEMs that offer L2 automated driving features on their production vehicles. Most OEMs listed are either preparing production plans for low level automation and/or are developing L4 technologies and often in parallel.

Fleet Layer

The middle layer are companies (non-OEM) developing complete AV vehicles in one form or another. Many companies in the middle space are integrating systems from multiple suppliers and coupling that with their own AV technology stack. Many of the builds are designated for fleet-based operation as either ride sharing platforms, robo-taxis or shuttles designed to operating within a restricted environment. Some of the largest fleets and fleet service providers occupy this space. As well there are many startups that are partnering with various suppliers to build out an AV for transit.

AV Domain Control Layer

The third layer are AV Stacks which are largely processor-based domain controllers that can handle the tasks of perception, decision and control.  Most include a hardware reference design coupled with software development kit (SDKs) but not all of them are necessarily end-to-end complete even though they are listed in this category.  Here you see a mix of processor companies that provide a reference design along with a software/hardware package for the buildup AV functionality. Other than processor companies, many tier-ones compete in this space with a production or near-production platform.  

Processor Stack

The processing stack is a crowded space already and more are expected to enter.  While many types of processors are called out in this space many are specialized processor logic that is optimized for parallelism which can process many streams of data in parallel.  Other processor architectures are specialized accelerators for graphics processing, image recognition or artificial intelligence. Once again, the common thread is parallelism in which massive amounts of data and instructions can be processed at once. Some of the processors represented in this domain are SoCs where they contain multiple processor architecture in one chip. Some include DSP technologies, or FPGA fabric from which new instruction sets can be dynamically changed. Some companies in the space may be providers of processor IP where their instruction sets can be licensed and burned onto a host chip or FPGA.   

Sensor Stack

Not surprising the sensor stack is the most crowded of them all.  Here you have a continuous stream of new entrants many of which are developing lower cost Lidar sensors.  The Lidar space is getting crowded even though the actual deployment of Lidar is still prohibitory expensive except for commercial fleets where you can afford a more expensive sensor package.

Lidar companies are dominated by those developing solid state devices that have some means of beam steering.  Lower cost flash Lidar devices are also coming which are basically limited resolution that can identify the presence of an object but cannot classify or produce a point cloud.  

Meanwhile, radar is still widely relevant and even gaining in this space because of new developments.  Here you have a handful of small form factor millimeter wave radar units that have flexible antenna configurations that can yield more granularity than previous radar.

Camera sensors are widely represented by the companies in this domain.  In the case of camera, you have many companies that make imagers -- the sensor chip that is uses a raw component in a sensor package whether it be a module or a board level product that couples sensing with some processing.  Image processing requires an exhaustive pipeline of processing power and new image sensors can handle some of the preprocessing requirements while still sending raw data to a central architecture. 

This space is also represented by companies that offer sensor fusion applications whether it be hardware or software.  But most sensor fusion applications still fuse the object data, not the raw data. Further, in many cases, smart sensors produce object data within the sensors, while other sensors send raw data to the main processor — where objects are produced before it is ingested into the fusion engine.

Data Connectivity Stack

The data connectivity stack is a combination of hardware and software solutions that support the movement of data along the in-vehicle networks or via wireless networks outside the vehicle. Here you have various tier-ones that make connectivity modules (ECUs) that can handle the data traffic, compress/decompress or encrypt messages where needed. Others in the space produce network interfaces and switches that may be a component within the connectivity space.  Meanwhile, wireless modem makers are a vital member of the data connectivity stack as future AVs must maintain connections to service providers and data centers because they are always talking to the “mothership.”

Mapping Stack

The mapping stack is a combination of various localization assets necessary to improve the performance and safety of automated vehicles. Precision map data enables the vehicle to localize with greater precision using a geocoded refernce points, images our clouds. This also reduces reliance on image sensors tracking lane markings.  And while lane markings are the most common way to determine where a vehicle is supposed to be, foul weather will prevent AVs from operating properly if relying exclusively on this. 

Maintaining up-to-data map data is forever a challenge for the mapping companies so new technologies are emerging that allow fleets of vehicles to pick up on changes to the road infrastructure. As a result, there are companies emerging that allow the aggregation of real time road conditions so that this information can be dynamically relayed to oncoming vehicles.

Algorithm Stack

The algorithm stack is a collection of software assets used throughout the AV processing pipeline. Many of the companies in this space offer algorithms for the perception side of the equation and here the task is further divided by the different sensors that are used. 

Additionally, the process of modeling the environment in real time requires advanced perception algorithms which may apply neural networks. Behind the scenes in AI are sophisticated training methods that create new inference models which are essentially AI-based algorithms that get redeployed to the AVs.

AI is one of the hottest areas within the field of automation because AI-based algorithms have the capacity to reason with partial information. Furthermore, the collection of data from fleets of vehicles enable the algorithms to improve with time as fleets are constantly collecting information on new scenes that may not have been thought of in previous training exercises.

Within the algorithm space there lots of new developments with respect to the full software stack. Several companies are offering stacks that support the whole processing pipeline. These tightly integrated software stacks relieve automakers from taking on the tough task of developing their own algorithms.

Safety and Security

Within the context of safety and security there are a diverse set of requirements that are necessary to either secure the safe performance of the vehicle, as well as protect the vehicle from malware and outside security threats. 

On the safety side you have functional safety which is a process from which AV technologies are designed and developed. Functional safety is critical for automotive best practices and many of the companies in this space offer safety rated components (either hardware or software) that are designed to minimize malfunctions, spot abnormal behavior and even instruct a safe failure. Many of these technologies are applied to the runtime components deep within the software stack (such as RTOS).

Meanwhile, within the context of cyber security, you have a lot of development that assures protection from malware and other cyber threats. While measures for cyber security are applied at many places throughout the hardware and software stacks, there are sophisticated cyber protection layers that are dedicated to the task of identifying and shutting down unauthorized code or instructions.

Development Tools 

Development tools are vital for designing sophisticated AV systems. Within this category you have several tools necessary for modeling the performance requirements of the system long before anything gets built. 

For the development of the systems there are many companies that offer simulation tools for testing the performance of the systems in a virtual environment.  These tools are used for testing the performance against a virtual environment where scenes, actors, sensors, and physics can be modeled. Some of the simulations offer the ability to test the individual components while other are used to test the performance of algorithms.

Within the development stack you also have lots of tools for the development of runtime components. These are often called code checkers that look for errors or anomalies in the code base and optimize it for deployment against a target hardware stack.   Additionally, there are many signal management tools that are used for timing and synchronization of processes from sensor fusion, to ECU to ECU communications.

About VSI

VSI was established in 2014 to provide industry with deep insight and analysis on the enabling technologies used for active safety and automated driving. Today, VSI is considered one of the industry’s top advisors by supporting R&D and planning departments within major automotive companies and suppliers worldwide.

VSI provides various research portals that support product & technology planning, competitive analysis, IP discovery, and product development.  VSI recently introduced a new service (VSI Pro) that offers a thorough decomposition of AV technologies through hands-on development and buildup of its own automated vehicle platform. VSI also conducts functional examination of critical enablers including sensors, domain controllers, and AV software development kits. Learn more at

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