The widespread development of driverless vehicles has led to the formation of autonomous racing competitions, where the high speeds and fierce rivalry in motorsport provide a testbed to accelerate technology development. A particular challenge for an autonomous vehicle is that of identifying a target trajectory - or in the case of a racing car, the ideal racing line. Many existing approaches to identifying the racing line are either not the time-optimal solutions, or have solution times which are computationally expensive, thus rendering them unsuitable for real-time application using on-board processing hardware. This paper describes a machine learning approach to generating an accurate prediction of the racing line in real-time on desktop processing hardware. The proposed algorithm is a dense feed-forward neural network, trained using a dataset comprising racing lines for a large number of circuits calculated via a traditional optimal control lap time simulation. The network is capable of predicting the racing line with a mean absolute error of +/-0.27m, meaning that the accuracy outperforms a human driver, and is comparable to other parts of the autonomous vehicle control system. The system generates predictions within 33ms, making it over 9,000 times faster than traditional methods of finding the optimal racing line. Results suggest that a data-driven approach may therefore be favourable for real-time generation of near-optimal racing lines than traditional computational methods.
Roborace team SIT Acronis Autonomous suffered a "computer says no" moment on Thursday when its race car drove straight into a wall, mere seconds after it had started driving. If you're familiar with the Little Britain T.V. show, you'll understand the meaning of "computer says no." And it couldn't be more true for this moment. Luckily no one was hurt. But, you live and you learn, and this is one of the ways people working in robotics learn how to improve their systems.
Robots still have some trouble handling the basics when put to the test, apparently. Roborace team SIT Acronis Autonomous suffered an embarrassment in round one of the Season Beta 1.1 race after its self-driving car abruptly drove directly into a wall. It's not certain what led to the mishap, but track conditions clearly weren't at fault -- the car had been rounding a gentle curve and wasn't racing against others at the same time. It wasn't the only car to suffer a problem, either. Autonomous Racing Graz's vehicle had positioning issues that got it "lost" on the track and cut its race short.
Balancing performance and safety is crucial to deploying autonomous vehicles in multi-agent environments. In particular, autonomous racing is a domain that penalizes safe but conservative policies, highlighting the need for robust, adaptive strategies. Current approaches either make simplifying assumptions about other agents or lack robust mechanisms for online adaptation. This work makes algorithmic contributions to both challenges. First, to generate a realistic, diverse set of opponents, we develop a novel method for self-play based on replica-exchange Markov chain Monte Carlo. Second, we propose a distributionally robust bandit optimization procedure that adaptively adjusts risk aversion relative to uncertainty in beliefs about opponents' behaviors. We rigorously quantify the tradeoffs in performance and robustness when approximating these computations in real-time motion-planning, and we demonstrate our methods experimentally on autonomous vehicles that achieve scaled speeds comparable to Formula One racecars.
A robotics startup that designs bionic limbs for children in the style of superheroes has raised £4.6 million from investors including the Formula 1 team Williams. Bristol-based Open Bionics became the best-selling multi-grip bionic hand in the UK after launching its Hero Arm in 2018, and plans to use the funding to grow to international markets. Using 3D scanning and 3D printing technologies, the firm has managed to drastically reduce the cost of building robotic prosthetics, allowing the bionic limbs to be covered by national healthcare systems in the UK and abroad. "The Hero Arm is a custom made myoelectric prosthetic. This means users, amputees and people with limb differences below the elbow, can control their new bionic fingers by squeezing the muscles in their forearms," Open Bionics co-founder Samantha Payne told The Independent.
Once a year, the bucolic grounds of Goodwood House in West Sussex, England, are consumed by the smell of exhaust fumes, the sound of engines revving, and an excited crowd of 100,000 people, all wanting a look at the special cars on show. They gather here because Charles Gordon-Lennox, the 11th Duke of Richmond, likes to occasionally open his home to host the Goodwood Festival of Speed, a celebration of all the history, the heritage, and the future of motor racing. This week, among the supercars, hypercars, and pure racing cars, Goodwood visitors will spot a low, black machine streaking in near silence up the winding driveway to the estate, which for the event is transformed into a 1.16-mile hill climb track. "We're pretty sure when the car appears, people will freak out," says Rod Chong, deputy CEO of Roborace. And it will be the first machine to give the hill climb a try without a human in command, so there are some nerves.
Ever since Roborace unveiled plans for driverless track cars, there's been a lingering question: can its technology outpace a human? The answer is a solid "no..." for now. The company used the recent Formula E race in Rome to pit its DevBot prototype car against pro drifter Ryan Tuerck, and the fleshy driver was clearly the frontrunner with a roughly 26-second lead -- you can see him claiming victory in the video below. That's still in the ballpark of what you'd expect from humans, but they wouldn't be lining up sponsorships after that kind of performance. You might not want to be too confident about humanity's motorsport prowess.
The Argentinian summer Sun beat down on the Buenos Aires city circuit as the cars approached the penultimate turn. It was February 18, 2017, the Saturday of Formula E's South American weekend, and two cars jostled for first place. The second car, though, was being too aggressive. Nearing the corner's apex, the vehicle misjudged its position and speed. The vehicle slammed into the blue safety walls surrounding the track. As the wreckage crumpled to a stop, a detached wheel rolled freely across the hot asphalt.
Roborace, the firm hoping to kick-start the future of driverless racing, has demonstrated its electric, 200-mile-per-hour (320km/h) self-driving car on a public track for the first time. The futuristic vehicle completed a lap of the Paris ePrix circuit ahead of the city's 2017 Formula E race, which took place on Saturday. The demonstration saw the car complete 14 turns of the almost 2 kilometre (1.2 mile) track while driven entirely by AI and sensors. The Robocar weighs almost 1,000 kilograms (2,200 lbs), and measures 4.8 metres long (15.7 ft) and two metres wide (6.5 ft). Four motors, each with 300kW of power and a 540kW battery, allow the car to reach dizzying speeds of over 320kph (200mph).