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A Low-Cost, Open Platform for End-to-End Autonomous Driving on a Miniature Ackermann Vehicle

Paper recorded by Signals 4 on 2026-09-03 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-03 on arXiv · recorded by Signals 4 on 2026-09-04

Category: cs.AI · 人工智能 · first seen 2026-09-04

Abstract

This paper presents a low-cost, open experimental platform for research in end-to-end autonomous driving with miniature Ackermann vehicles. The platform combines a physical vehicle, a printed urban track, data collection tools, trajectory registration, and a Webots digital twin, enabling controlled experiments that connect simulation-based autonomous-driving methods to real-world execution. As a f

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#194 most recent of 300 cs.AI papers we have recorded · ↑ newer: Terminal-Universe: Turning Agent Trajectories into Scalable Terminal E · ↓ older: Efficient Test-Time Adaptation through Human-AI Interaction
Cite this page: A Low-Cost, Open Platform for End-to-End Autonomous Driving on a Miniature Ackermann Vehicle: the #194 most recent of 300 cs.AI papers we have recorded (as of 2026-09-03). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/a-low-cost-open-platform-for-end-to-end-autonomous-driving-on-a-miniature-ackerm.html
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