Researchers from the BME Formula Racing Team present the autonomous software stack of the FRED-003C, which enabled full-scale autonomous racing. The software stack was developed in the context of Formula Student Driverless competitions. The paper details the software pipeline, hardware-software architecture, and methods for perception, localization, mapping, planning, and control. Why it matters: The team's experience contributed to their participation in the Abu Dhabi Autonomous Racing League, and sharing the system provides a valuable starting point for other students in the region.
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.
MBZUAI faculty Eric Xing and Qirong Ho are developing AI operating systems (AI OS) for efficient AI development, similar to mobile OS. They co-founded AI startup Petuum and lead the CASL community, which focuses on composable, automatic, and scalable learning. CASL provides a unified toolkit for distributed training and compositional model construction, with contributions from MBZUAI, CMU, Berkeley, and Stanford. Why it matters: The development of AI OS aims to optimize AI applications by efficiently connecting software and hardware, fostering innovation and broader adoption of AI solutions across industries in the region.
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.
Holger Pirk from Imperial College London is developing a novel approach to data management system composition called BOSS. The system uses a homoiconic representation of data and code and partial evaluation of queries by components, drawing inspiration from compiler-construction research. BOSS achieves a fully composable design that effectively combines different data models, hardware platforms, and processing engines, enabling features like GPU acceleration and generative data cleaning with minimal overhead. Why it matters: This research on composable database systems can broaden the applicability of data management techniques in the GCC region, enabling more flexible and efficient data processing for various applications.
InfiAgent is a new agent framework comparable to GPT4-Agent, developed by replicating Codex. It includes InfiCoder, an open-source model for text-to-code, code-to-code, and freeform code-related QA tasks. The framework focuses on data analysis and integrates an LLM with programming capabilities and a sandbox environment for executing Python code. Why it matters: This research demonstrates the potential for advancements in AI operating systems and highlights areas where current models like GPT-4V can be improved, contributing to the broader development of more capable and versatile AI agents.