Driver-automation shared steering control considering driver neuromuscular delay characteristics based on stackelberg game

Liu, Ahao; Guo, Hongyan; Shi, Wanqing; Dai, Qikun; Zhang, Jiaming

doi:10.1016/j.geits.2022.100027

Cited by 5 publications

(1 citation statement)

References 37 publications

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“…Research in intelligent road transportation systems mainly focuses on these vehicles due to their versatility and increased throughput. However, with the developments in the EV and AV industry, there have been rising concerns about driver safety by manufacturers, operators, and researchers [5,6].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of You Only Look Once v4 in Road Anomaly Detection and Visual Simultaneous Localisation and Mapping for Autonomous Vehicles

Bala,

Adeshina,

Aibinu

2023

WEVJ

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The proliferation of autonomous vehicles (AVs) emphasises the pressing need to navigate challenging road networks riddled with anomalies like unapproved speed bumps, potholes, and other hazardous conditions, particularly in low- and middle-income countries. These anomalies not only contribute to driving stress, vehicle damage, and financial implications for users but also elevate the risk of accidents. A significant hurdle for AV deployment is the vehicle’s environmental awareness and the capacity to localise effectively without excessive dependence on pre-defined maps in dynamically evolving contexts. Addressing this overarching challenge, this paper introduces a specialised deep learning model, leveraging YOLO v4, which profiles road surfaces by pinpointing defects, demonstrating a mean average precision (mAP@0.5) of 95.34%. Concurrently, a comprehensive solution—RA-SLAM, which is an enhanced Visual Simultaneous Localisation and Mapping (V-SLAM) mechanism for road scene modeling, integrated with the YOLO v4 algorithm—was developed. This approach precisely detects road anomalies, further refining V-SLAM through a keypoint aggregation algorithm. Collectively, these advancements underscore the potential for a holistic integration into AV’s intelligent navigation systems, ensuring safer and more efficient traversal across intricate road terrains.

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Section: Introductionmentioning

confidence: 99%