Zobrazeno 1 - 10
of 11 534
pro vyhledávání: '"Autonomous Driving"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-17 (2024)
Abstract Targeting the lateral motion control problem in the intelligent vehicle autopilot structural system, this paper proposes a feedforward + predictive LQR algorithm for lateral motion control based on Genetic Algorithm (GA) parameter optimisati
Externí odkaz:
https://doaj.org/article/3a886379a5b9472b9e0f4b0f60746188
Publikováno v:
Journal of Big Data, Vol 11, Iss 1, Pp 1-30 (2024)
Abstract This paper addresses the often overlooked issue of fairness in the autonomous driving domain, particularly in vision-based perception and prediction systems, which play a pivotal role in the overall functioning of Autonomous Vehicles (AVs).
Externí odkaz:
https://doaj.org/article/a07f2e2bed054f63b830b617fa2f6473
Publikováno v:
Chengshi guidao jiaotong yanjiu, Vol 27, Iss 9, Pp 334-337 (2024)
Objective In the semi-independent right-of-way operation scenario of the tram, there exists the potential risk of pedestrians or vehicles entering the track area, especially in the intersection area. The sensing system deployed on the tram and the gr
Externí odkaz:
https://doaj.org/article/a4a61b7794324f6993817eb8484c5fb9
Publikováno v:
Vehicles, Vol 6, Iss 3, Pp 1364-1382 (2024)
Accurate vehicle detection is crucial for the advancement of intelligent transportation systems, including autonomous driving and traffic monitoring. This paper presents a comparative analysis of two advanced deep learning models—YOLOv8 and YOLOv10
Externí odkaz:
https://doaj.org/article/b3e9412c267e429db0071f3e7c660cbc
Autor:
Julius Bächle, Jakob Häringer, Noah Köhler, Kadir-Kaan Özer, Markus Enzweiler, Reiner Marchthaler
Publikováno v:
Autonomous Intelligent Systems, Vol 4, Iss 1, Pp 1-13 (2024)
Abstract This article introduces an open-source software stack designed for autonomous 1:10 scale model vehicles. Initially developed for the Bosch Future Mobility Challenge (BFMC) student competition, this versatile software stack is applicable to a
Externí odkaz:
https://doaj.org/article/b1269355c80a4c7292b7d5d66511aadb
Publikováno v:
IET Intelligent Transport Systems, Vol 18, Iss 8, Pp 1477-1493 (2024)
Abstract A lightweight, high‐definition vector map (HDVM) enables fully autonomous vehicles. However, the generation of HDVM remains a challenging problem, especially in complex urban scenarios. Moreover, numerous factors in the urban environment c
Externí odkaz:
https://doaj.org/article/36a1303fe6854f529b70334f0d4db3d4
Publikováno v:
IET Intelligent Transport Systems, Vol 18, Iss 8, Pp 1445-1458 (2024)
Abstract Aiming to address the challenge where existing methods struggle to predict accurate disparities for imperfectly rectified stereo images, and that supervised training requires a considerable amount of ground truth, a self‐supervised binocul
Externí odkaz:
https://doaj.org/article/3948519a1acd487c9223feacd52fb901
Publikováno v:
Industrial Robot: the international journal of robotics research and application, 2024, Vol. 51, Issue 4, pp. 632-639.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/IR-01-2024-0001
Publikováno v:
Alexandria Engineering Journal, Vol 109, Iss , Pp 497-507 (2024)
This study explores a more effective obstacle avoidance method for autonomous driving based on the monocular vision system of YOLOv5. The study utilizes the YOLOv5 model to detect obstacles and road signs in the environment in real-time, including ve
Externí odkaz:
https://doaj.org/article/596b663c8fa1450982d7f397298c8976
Publikováno v:
Alexandria Engineering Journal, Vol 106, Iss , Pp 298-311 (2024)
Object detection in road scenarios is crucial for intelligent transport systems and autonomous driving, but complex traffic conditions pose significant challenges. This paper introduces Z-You Only Look Once version 8 small (Z-YOLOv8s), designed to im
Externí odkaz:
https://doaj.org/article/a484d38475514a11b7a2475814ea83d5