Making AI smart enough for autonomous vehicles

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Making AI smart enough for autonomous vehicles A mix of computer science companies, leading tech firms and worldwide automakers are working hard to create a next-generation artificial intelligence (AI) using machine learning (ML) that will make fully autonomous vehicles a reality.

Already, luxury car makers such as Mercedes-Benz, BMW, Lexus and Tesla, and tech firms such as Google and Microsoft, have sophisticated advanced driver-assistance systems (ADAS) that can provide an autonomous experience in urban environments up to SAE Level 3. These systems cover a few driving situations based on narrow drive requirements, although all still rely on human intervention in a stressful situation or in the event of an imminent accident.

As of spring 2019, the European Union’s Commissioner for Transport, Violeta Bulc, said she expects fully autonomous driving capabilities to arrive by 2030.1 The European Road Transport Research Advisory Council2, however, predicts these capabilities will not arrive until after 2030.

To achieve full autonomy — a state in which the vehicle can navigate itself through any terrain in any conditions — it’s necessary to take AI approaches to the next level, with driving agents that implement human driving behaviour by collecting driver demonstrations that optimise driving policy for unknown or unpredictable situations. This requires enormous amounts of data, so data engineers must find ways to eliminate the processing of so-called “boring” data that will not help the system develop better driving strategies.

For truly safe and secure autonomous driving that delivers on the promise of fewer accidents and enhanced sustainability, autonomous vehicles must be able to sift out extraneous data so they can make driving decisions better and faster than humans — 100 percent of the time. Smart road infrastructure attempts to address this problem by delivering only the data needed, but it also creates a dependency that inhibits development of a truly autonomous vehicle. The data, compute and AI issues ahead are extremely challenging.

But the upside is tremendous, because autonomous cars have great potential to save lives. The U.S. National Highway Traffic Safety Administration (NHTSA)3 contends that automated vehicles can reduce injuries based on one critical fact: 94 percent of serious auto crashes are caused by human error. Autonomous vehicles promise to eliminate a big part of human error from the crash equation, which will help protect drivers and passengers, as well as bicyclists and pedestrians.

1 Forbes, April 6, 2019: “Self-driving cars in 10 years: EU Expects ‘Fully Automated’ cars by 2030” 2 Forbes, May 21, 2019: “Self-Driving Automobiles: How Soon and How Much?” 3 NHSTA: “Automated Vehicles for Safety”

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Disrupting the status quo

Today, extensive preparation4 is underway, with about half of the U.S. states testing autonomous vehicles on public roads. Most of the testing is in Arizona, California, Georgia, Michigan, Nevada, Texas, Pennsylvania and Washington. California has emerged as the busiest state, with 52 companies testing autonomous vehicles in the Golden State alone.

A new approach to AI begins with a baseline autonomous driving platform that enables driving behaviour engineers to establish driver behaviour knowledge that can be refined to simulate how an intelligent driving agent (the autonomous car) would respond in real-world situations. With this process, engineers can aid the intelligent driving agent to learn driving behaviour and build driving functions and recall that information over time, similar to human learning.

Developing AI driving agents to learn

Today, computer scientists experiment with different types of approaches, architecture and training strategies in their incorporation of AI and ML — all of which will play a role in the development of autonomous vehicles over the next decade or two. The main AI/ML theories relevant for teaching an autonomous driving agent include these:

1. Brain science. One of the possible approaches to build better AI driver agents draws its inspiration from brain science research conducted by Danko Nikolic at the Frankfurt Institute for Advanced Studies (FIAS) in Germany. Nikolic, in the Practopoiesis,5 theorises that humans actually learn at three levels: trial and error (accumulating knowledge throughout evolution); learning how to learn; and finally, putting the knowledge into action. It’s the second level at which humans “learn how to learn” that Nikolic considers the most important application in applying AI to autonomous driving. If we can discover more about how humans learn, there is greater potential for AI as it applies to autonomous cars and for education in general. Nikolic’s theory maintains that we must teach AI technology to learn the way humans do.

Adapting this theory to AI in the automotive realm starts by building driving-learning agents capable of learning the way humans do, then creating small, fast-learner training sets from human driver demonstrations. The concept of adapting innovative brain research to AI and the production of fully autonomous vehicles has emerged as one of the more exciting technology innovations in the past couple of years. While it will take time, the benefits to society of producing self-driving vehicles — and learning more about how the brain works along the way — hold great promise for human progress.

The brain science approach can potentially take the best of human learning and develop the best AI driver agent for the automotive field, but the research can take several years, and it’s difficult to adapt it to the production of autonomous driving.

4 Curbed.com, March 8, 2019: “Are self-driving cars safe for our cities?” 5 Danko Nikolic, “Practopoiesis”

Autonomous vehicles must be able to sift out extraneous data so they can make driving decisions better and faster than humans — 100 percent of the time.

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2. Imitation learning. In this type of approach, the AI learner first observes the actions of the driving expert, often a human, in supervised learning. The AI learner then uses this training set to learn a policy that tries to mimic the actions demonstrated by the expert to achieve the best performance. In practice, the AI imitation learning agent would observe a human expert driver and register the driver’s actions over time, recording how he or she makes right and left turns, stops at stop signs and accelerates on the open highway. Based on those tracked results, the agent would create a policy of what actions to take for any given situation based on what the expert did. At a run-time test, it would compute the best action based on the developed policy. Over time, the system develops a network of best cases and, based on experience, it theoretically would always pick the best option based on a deterministic policy model in a context of inference.

Imitation learning leverages the benefits of supervised learning, but struggles with an important issue: how to collect expert driving situations and sum up steering errors in a way that allows the driving agent to handle off-course situations that it has never dealt with before, while keeping safety as one of its major principles. It’s possible to overcome this problem, but it requires advanced autonomous driving technology, a full team of expert technologists and lots of time.

3. Reinforcement learning. With this type of learning, there’s no expert, human or otherwise; it’s based on unsupervised learning. The agent is assigned a reward function and uses various strategies to effectively explore the different states and actions on the road. Via trial and error, it comes up with the optimum policy. In reinforcement learning, we try to maximise the rewards for the agent’s actions. In driving mode (model inference), the reinforcement learning agent executes the agent’s model for every event and passes with no issues.

However, if during a test the car crashes or hits a pedestrian or another car in a simulation, the task ends with negative rewards. The agent will then start with random actions, and through trial and error learn which actions maximise rewards and which actions result in a positive maximised score. When reinforcement learning works well, if the car runs into a tree, it will learn not to do that again and will then avoid repeating that mistake. The main challenges of the reinforcement learning are the low stability in behaviour and high dependency on the size of the environment and the computer processing power.

Overcoming catastrophic forgetting

Neural networks are computer systems designed to behave as a human brain; however, being artificial does not mean they do not forget. Catastrophic forgetting refers to the catastrophic loss of previously learned driving knowledge whenever neural networks are trained with a single new additional response. Under the theory of catastrophic forgetting, depending on the architecture, the agent neural network can only run a small number of scenarios before its performance rapidly decreases.

Today, if a team puts more test scenarios on a neural network, it will top out at a certain threshold. In application today, nobody really knows how to fix this except to add scenario classifier input and preconditions to the driving behaviour agent. However, based on probability theory, this decreases accuracy.

Resolving catastrophic forgetting promises to take autonomous driving to the highest possible level — fully autonomous driving.

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There’s currently research in the United States and China addressing this challenge, but until this issue of catastrophic forgetting is overcome, it will not be possible to address fully autonomous driving through one unified behaviour model. Research has begun on how to avoid catastrophic forgetting, but there is no definitive method to expand the ability to train and manage end-to-end unlimited scenarios. This is very important; if a neural network forgets what it has learned based on previous experiences, autonomous vehicles would have to stick with the existing rule-based programming model and never reach full autonomy.

Resolving catastrophic forgetting promises to take autonomous driving to the highest possible level, fully autonomous driving. But it’s very complex and difficult to get an AI driving agent past a certain threshold.

Around the corner

Industry observers often wonder which of these AI approaches will prevail in the long run. Most likely, we shall see some of all four used over the next two decades. Driving behaviour data engineers, computer scientists, and AI scientists and researchers must find the proper design — or even new AI approaches — to develop fully autonomous driving. However, as we run more tests, we will know more about learning how to learn and how to generalise and evolve as we gain knowledge.

The challenge of getting autonomous driving behaviour to the next level presents too many data and processing issues for us to grapple with all of human intelligence. Humankind has yet to create a storage facility that can collect and hold all that data. But if computer scientists and industry researchers stay focused on AI agents for autonomous driving, we can overcome many of these challenges and we shall begin seeing the first wave of fully autonomous vehicles that will offer higher levels of autonomous driving functions in the near future.

All this new autonomous driving technology will be developed on back-end autonomous driving platforms that include compute, data lake, unified security, fully managed development security operations (DevSecOps), metadata management and governance, and high-performance networks. Any enhanced AI system will have to handle hundreds, if not thousands, of petabytes of data and at least thousands of graphics processing units (GPUs). The computer industry has begun to address many of these data storage and processing performance needs.

We will continue to make bigger and faster computers, but mastering the power of AI and applying it to autonomous driving will keep automotive computer science engineers busy for decades. It’s one of the great challenges of our generation.

Driving behaviour data engineers, computer scientists, and AI scientists and researchers must find the proper design — or even new AI approaches — to develop fully autonomous driving.

6 All the AI approaches outlined in this paper are specific and focused on human driving behaviour capabilities. While the field of artificial general intelligence (AGI) targets a different science and research area and technologists can apply it to multiple industries and business processes, the approaches outlined in this paper focus solely on driving an autonomous car.

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About the author

Davor Andric is chief technology officer of Autonomous Driving, AI and head of Robotic Drive Engineering of DXC Technology. Over the past 20 years, Davor has been working in the consulting, software and technology space. His expertise is in designing and building scalable platforms for Automotive, ML and AI, and building products on those platforms.

Learn more at www.dxc.technology/AVTech

About DXC TechnologyDXC Technology, the world’s leading independent, end-to-end IT services company, manages and modernises mission-critical systems, integrating them with new digital solutions to produce better business outcomes. The company’s global reach and talent, innovation platforms, technology independence and extensive partner network enable more than 6,000 private- and public-sector clients in 70 countries to thrive on change. For more information, visit www.dxc.technology.

© 2019 DXC Technology Company. All rights reserved. LO_3105a-20. November 2019www.dxc.technology

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