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A Review of Full-Sized Autonomous Racing Vehicle Sensor Architecture
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In the landscape of technological innovation, autonomous racing is a dynamic and challenging domain that not only pushes the limits of technology, but also plays a crucial role in advancing and fostering a greater acceptance of autonomous systems. This paper thoroughly explores challenges and advances in autonomous racing vehicle design and performance, focusing on Roborace and the Indy Autonomous Challenge (IAC). This review provides a detailed analysis of sensor setups, architectural nuances, and test metrics on these cutting-edge platforms. In Roborace, the evolution from Devbot 1.0 to Robocar and Devbot 2.0 is detailed, revealing insights into sensor configurations and performance outcomes. The examination extends to the IAC, which is dedicated to high-speed self-driving vehicles, emphasizing developmental trajectories and sensor adaptations. By reviewing these platforms, the analysis provides valuable insight into autonomous driving racing, contributing to a broader understanding of sensor architectures and the challenges faced. This review supports future advances in full-scale autonomous racing technology.
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Cited by 1 Pith paper
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A Hierarchical Test Platform for Vision Language Model (VLM)-Integrated Real-World Autonomous Driving
A hierarchical real-world testing platform for VLM-integrated autonomous driving is demonstrated on a by-wire vehicle, with a cloud GPT-4 agent making high-level decisions inside an Autoware stack.
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