When race car drivers take tight turns at high speeds, they rely on their experience and gut feeling to hit the gas pedal without spinning out. But how does an autonomous race car make the same decision?
Currently, many autonomous cars rely on expensive external sensors to calculate a vehicle’s velocity and chance of sideslipping on the racetrack. In a different approach, one research team in Switzerland has recently developed a novel a machine learning algorithm that harnesses measurements from more simple sensors. They describe their design in a study published August 14 in IEEE Robotics and Automation Letters.
As a race car takes a turn around the track, its forward and lateral velocity determines how well the tires grip the road—and how much sideslip occurs.
“(Autonomous) race cars are typically equipped with special sensors that are very accurate, exhibit almost no noise, and measure the lateral and longitudinal velocity separately,” explains Victor Reijgwart, of the Autonomous Systems Lab at ETH Zurich and a co-creator of the new design.
These state-of-the art sensors only require simple filters (or calculations) to estimate velocity and control sideslip. But, as Reijgwart notes, “Unfortunately, these sensors are heavy and very expensive—with single sensors often costing as much as an entry-level consumer car.”
His group, whose Formula Student team is named AMZ Racing, sought a novel solution. Their resulting machine learning algorithm relies on several measurements including: two normal inertial measurement units, the rotation speed and motor torques at all four wheels, and the steering angle. They trained their model using real data from racing cars on flat, gravel, bumpy, and wet road surfaces.
In their study, the researchers compared their approach to the external velocity sensors that have been commonly used at multiple Formula Student Driverless events across Europe in 2019. Results show that the new approach demonstrates comparable performance when the cars are undergoing a high level of sideslip (at 10◦ at the rear axle), but offers several advantages. For example, the new approach is better at rejecting biases and outlier measurements. The results also show that the machine learning approach is 15 times better than using just simple algorithms with non-specialized sensors.
“But learning from data is a two-edged sword,” says Sirish Srinivasan, another AMZ Racing member at ETH Zurich. “While the approach works well when it has been used under circumstances that are similar to the data it was trained on, safe behavior of the [model] cannot yet be guaranteed when it is used in conditions that significantly differ from the training data.”
Some examples include unusual weather conditions, changes in tire pressure, or other unexpected events.
The AMZ Racing team participates in yearly Formula Student Driverless engineering competitions, and hopes to apply this technique in the next race.
In the meantime, the team is interested in further improving their technique. “Several open research questions remain, but we feel like the most central one would be how to deal with unforeseen circumstances,” says Reijgwart. “This is, arguably, a major open question for the machine learning community in general.”
He notes that adding more “common sense” to the model, which would give it more conservative but safe estimates in unforeseen circumstances, is one option. In a more complex approach, the model could perhaps be taught to predict its own uncertainty, so that it hands over control to a simpler but more reliable mode of calculation when the AI encounters an unfamiliar scenario.