arXiv ·
Team TII EuroRacing (TII-ER) developed a full autonomous software stack for oval racing, enabling speeds above 75 m/s (270 km/h). The software includes modules for perception, planning, control, vehicle dynamics modeling, simulation, telemetry, and safety. The team achieved second and third place in the first two Indy Autonomous Challenge events using this stack.
TII ·
TII-EuroRacing Team, comprised of researchers from TII's ARRC and UNIMORE, is participating in the Indy Autonomous Challenge (IAC) at CES 2023 in Las Vegas. The team will compete with its DO12 racecar, a Dallara AV-21 retrofitted with automation hardware and advanced prototype software. The IAC aims to accelerate the commercialization of fully autonomous vehicles and advanced driver assistance systems. Why it matters: This participation allows TII to test and improve its autonomous vehicle technology in a dynamic environment, contributing to the advancement of autonomous systems in the region.
TII ·
Technology Innovation Institute (TII) and the University of Modena and Reggio Emilia are participating as Team TII-EuroRacing in the Autonomous Challenge at CES 2022 in Las Vegas. TII is also a premier sponsor of the event, which features head-to-head autonomous racecar competition at the Las Vegas Motor Speedway. Team TII-EuroRacing will compete with its DO12 racecar, a Dallara AV-21 retrofitted for automation, after making it to the finals at the Indy Autonomous Challenge in October 2021. Why it matters: This event highlights the UAE's commitment to advancing autonomous robotics and positions TII as a leader in the development of autonomous racing systems.
arXiv ·
This paper introduces a minimalistic autonomous racing stack designed for high-speed time-trial racing, emphasizing rapid deployment and efficient system integration with minimal on-track testing. Validated on real speedways, the stack achieved a top speed of 206 km/h within just 11 hours of practice, covering 325 km. The system performance analysis includes tracking accuracy, vehicle dynamics, and safety considerations. Why it matters: This research offers insights for teams aiming to quickly develop and deploy autonomous racing stacks with limited track access, potentially accelerating innovation in autonomous vehicle technology within the A2RL and similar racing initiatives.
arXiv ·
The TUM Autonomous Motorsport team developed algorithms and deployment strategies for the Abu Dhabi Autonomous Racing League (A2RL). Their software emulates human driving behavior, pushing vehicle handling and multi-vehicle interactions. The team's approach led to a victory in the A2RL challenge. Why it matters: Autonomous racing serves as a valuable research environment for advancing autonomous driving tech and improving road safety in the region and globally.
arXiv ·
This paper introduces a longitudinal control system for autonomous racing vehicles with combustion engines, translating trajectory-tracking commands into low-level vehicle controls like throttle, brake pressure, and gear selection. The modular design facilitates integration with various trajectory-tracking algorithms and vehicles. Experimental validation on the EAV24 racecar during the Abu Dhabi Autonomous Racing League at Yas Marina Circuit demonstrated the system's effectiveness, achieving longitudinal accelerations up to 25 m/s². Why it matters: This research contributes to the advancement of autonomous racing technology in the region, showcasing practical applications in high-performance scenarios and fostering innovation in vehicle control systems.