Hypothetically, if cars were being invented today they would have no human behind the wheel! Take the driver out of the loop and you will solve 90% of traffic fatalities experts claim. A fully automated city is quieter, cleaner, safer and more efficient! What’s not to like?
But solving this problem it not easy. Automation requires the most advanced deployment of embedded systems backed by a network of dynamic algorithms and fully connected to maintain the performance and safety of the vehicle grid.
Automation is worth trillions when you look at the big picture. Literally every company from technology, transportation, telecommunications, to logistics, data center, IT and commerce are looking to capture a piece of this enormous opportunity.
While everyone is familiar with the advances being made by the big guys like Waymo (Google), Uber, and GM, there are literally hundreds of other companies creating solutions and IP to serve automated vehicle technologies.
Many of these solutions come from start-ups, some spun out of university projects related to artificial intelligence, localization or data sciences. Fueled by an endless supply of investment capital and venture funds, the eco-system for automated driving is on fire.
The Extended AV Eco-System
The composition of companies competing in the AV Eco-System is very diverse as pointed out in the following chart which is divided into four quadrants. The lower left is where the vehicles and associated robot technologies reside. This is largely the AV eco-system as we know it today and is the basis for VSI’s Eco-System chart on the home page of our portal.
On the upper left side of the chart are the data center assets to support the entire pipeline of data coming into and out of the vehicle. From server farms to telecom this area handles the data flow.
The upper right side of the chart represent the services necessary to support automation and includes mobility services, commerce, localization services, app services, and more. The lower right side represents functions such as fleet management, supervision, monitoring, and public transport.
The Disruption Begins
Within the passenger car segment, the development of automated vehicles is following two different trajectories. Incremental automation is emerging now with many vehicles offering level 2 automation built on top of safety systems. The consumer passenger car segment will continue down this path for years to come before highly automated vehicle (L4+) starts putting a dent into traditional ownership models.
Once that happens you get a shift from private ownership to Automated Mobility on Demand (AMoD). From here the composition of the value chain starts changing drastically.
When traditional ownership begins to transition to AMoD, you will have fleet operators as the new owners of vehicle assets. Under this model, traditional auto will serve fleet operators who eventually become the primary customers of the vehicles. Fleet operators become the new buyers of thousands of vehicles and will have leverage to spec out vehicles that best meet their requirements.
Traditional auto suppliers including tier ones are vying for position as well. Their knowhow in systems and integration bodes well for automated vehicles. Advances in mechatronics, redundant systems, and fail-safe designs will drive their business going forward. Vehicles deployed for automated mobility on demand will be a different kind of car. Strong, sturdy, and redundant resulting in vehicles that will run 20+ hours a day.
Automated Mobility on Demand requires massive processing resources that are distributed throughout the vehicle. This is understood, but the processing architectures necessary to handle AI are on an order of magnitude greater than traditional approaches. Here, the big processor names are highly committed to developing the best AI architectures, but dozens of others are vying to create more powerful and more efficient AI accelerators.
Better Sensors… more data
Creating a perfect environmental model is vital for proper automation but no sensor is perfect. Lidar is the best at measuring the world in 3D, but Lidar is expensive and is not all-weather. Radar is cheaper than Lidar and getting better at detection of non-metallic actors but still suffers from noise. Camera is best at detection but suffers at judging distances and movements. Furthermore, camera performance declines in inclement weather.
Building the best sensor package will largely depend on the application. Level 4 applications for commercial fleet operators will require a full suite of sensors and as well as localization assets and precision mapping. The entire stack of hardware and software components for Level 4 vehicles will be worth substantially more than typical consumer vehicles with automated features.
More sensors mean more data and processing in real time and this is taxing to the AV stack. Furthermore, processing RAW sensor data is better suited for AI applications. On the other hand, processing on the edge (or in the sensor) may be more efficient for the AV stack since objects are classified and labeled before the come into the fusion pipeline.
The Eco-System Outside the Car
Getting to new mobility is going to take much more than outfitting vehicles with dozens of sensors and loads of intelligence. Making cars self-aware to the point of operating autonomously is largely been solved. But in the context of mobility services, there remains many gaps between the automated driving systems themselves and the digital infrastructure necessary to support fleets of automated vehicles.
Data center assets are going to be vital to support the eco-system outside of the vehicles. Therefore, large web-based storage and compute services are going to be very important to support the services and functions associated with Automated Mobility on Demand.
There are also a host of other constituents that will play a role in mobility services. From fleet management and maintenance, to support of services and transactions there are dozens of players that will be necessary to support both public and private interests.
To support this new eco-system outside the car there needs to be an orchestration layer to support the data and services that support the fleets of vehicles. Furthermore, this orchestration layer will require an open interface specification that facilitates outside services and players. Otherwise, the industry will remain vertically integrated and we don't that model will prevail.
Safety & Control
Safety and control is another big challenge to those building autonomous vehicles as you need a fallback in case things go wrong. The underpinning of an autonomous control system is a fault tolerant real-time operating system. Within this context some domains will be virtualized to optimize the computing architectures, while others will run in lockstep to reduce the chances of failure.
In the context of robotaxis, there is another level of safety that is outside the car and this is vital to Automated Mobility on Demand. Fleets of automated vehicles will require teleoperation and remote monitoring. Teleoperation will be based on monitoring services that optimize flow and traffic, or provide fall back when conditions in the environment are unpredictable, from storms, or natural disasters.
There is also an increasingly level of cyber security necessary to support Automated Mobility on Demand. Traditional vehicles don’t share the same threat level as highly automated vehicles but once fleets of vehicles become abundant so too do the threat levels. This is especially true with a services oriented architecture (SoA) where data is coming into and going out of the vehicles regularly. Furthermore, teleoperation becomes are critical threat area since an outside services provider has the authority to reach deep into the control commands or take over control entirely. The industry is going to require the most sophisticated application of security when highly automated vehicles reach critical mass.