Pith. sign in

REVIEW 1 cited by

A Review of Full-Sized Autonomous Racing Vehicle Sensor Architecture

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2402.02603 v1 pith:PUWEHFOQ submitted 2024-02-04 cs.RO

classification cs.RO
keywords autonomousracingsensorreviewadvancesanalysischallengesdetailed
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Hierarchical Test Platform for Vision Language Model (VLM)-Integrated Real-World Autonomous Driving

    cs.RO 2025-06 conditional novelty 4.0 of 10

    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.

Pith tools