INDY AUTONOMOUS CHALLENGE An important new addition to the simulation toolbox for all the IAC teams – and a pragmatic stepping stone to increasing the on-track car-count in IAC competitions – arrives in 2024. Series sponsor dSpace has created the Simphera software-in-the-loop setup such that teams will be able to compete in virtual races with high-fidelity models, either locally on a PC or in the cloud. Opponents will be either generic traffic cars, multiples of the team’s own AI drivers or, by arrangement, other teams. “Our goal is to create races in the cloud,” says Raimund Sprick, senior manager, automated driving and software solutions, dSpace. “We are fine-tuning the vehicle models so they will behave exactly as the real car, and recreating the track at Monza. Each team will have its own workspace where it can upload its controller and run the test in the cloud. The advantage there is that we can run several simulations in parallel.” “The real edge that they’re giving us is the ability to race against other teams and get an insight into how the other teams make these decisions,” comments ART’s Wolfe. “Being able to get that insider information and test your software against the other teams instead of only in your own validation pipeline or maybe benchmarking against yourself, you’ll see how you measure up against the others before we get to the competition cycle. It will be a very interesting twist in the way that people develop or try to predict what their opponents are going to do.” Above: dSpace Simphera track simulation in action Below: Virtual races allow teams to fine-tune their AI drivers The next step As part of its ongoing research, the AI Racing Tech team is exploring neurosymbolic high-level planning for the agent – learning-based approaches for learning high-level planning and behavior as well as parameter learning from the controls perspective. Says Wolfe, “This research approach allows for cross-platform-transferable autonomy. You can also learn the vehicle dynamics parameters of the agent in real time and then feed them back into your system. That way you can have better-tuned control systems for the agent behavior.” This could include exploiting tire models to feed information about the current behavior of the car on track into the AI driver’s decision making process. The problem, she says, is the “closed-source” nature of motorsport, which makes it hard to get the data in the first place – such as detail on how changes in the friction coefficients as the tires heat up affect the car’s behavior. “We want to be able to determine from the behavior of the car what those parameters are and feed them back into how we’re making decisions in our control stack,” Wolfe explains. “Dealing with how the car responds to the track conditions has become a competitive edge. Humans have a feel for how the car moves and behaves but it’s difficult to program a car to have that same intuition for how the system behaves. You can take data from real race car drivers and there’s a little bit of behavioral cloning in the way that you’re looking at executing the software stack, but the conditions are very varied and different. There’s no way to update it to understand the entire gamut of what that behavior should look like. “Determining some of these things in real time and trying to feed that back into the system for more intelligent decision making is an active research area for us and, I think, for all the teams,” she concludes. “Writing and optimizing code that can run in real time, that can inference fast enough for the driver and the behavior to react quickly enough, is a huge challenge.” Read more about dSpace on page 77 “OUR GOAL IS TO CREATE RACES IN THE CLOUD… WE ARE FINE-TUNING THE VEHICLE MODELS SO THEY WILL BEHAVE EXACTLY AS THE REAL CAR” Raimund Sprick, senior manager for AD and software solutions, dSpace The 2024 autonomous challenge at Las Vegas Motor Speedway 24 ADAS & Autonomous Vehicle International April 2024