Robust autonomous driving control using deep hybrid-learning network under rainy/snown conditions

Lee, Chao-Yang; Khanum, Abida; Sung, Tien-Wen

doi:10.1007/s11042-024-19601-1

Multimed Tools Appl

2024

DOI: 10.1007/s11042-024-19601-1

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Robust autonomous driving control using deep hybrid-learning network under rainy/snown conditions

Chao-Yang Lee,

Abida Khanum,

Tien-Wen Sung

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2024

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Ensuring Driving and Road Safety of Autonomous Vehicles Using a Control Optimiser Interaction Framework Through Smart “Thing” Information Sensing and Actuation

Almutairi,

Asmari,

Alqubaysi

et al. 2024

Machines

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Road safety through point-to-point interaction autonomous vehicles (AVs) assimilate different communication technologies for reliable and persistent information sharing. Vehicle interaction resilience and consistency require novel sharing knowledge for retaining driving and pedestrian safety. This article proposes a control optimiser interaction framework (COIF) for organising information transmission between the AV and interacting “Thing”. The framework relies on the neuro-batch learning algorithm to improve the consistency measure’s adaptability with the interacting “Things”. In the information-sharing process, the maximum extraction and utilisation are computed to track the AV with precise environmental knowledge. The interactions are batched with the type of traffic information obtained, such as population, accidents, objects, hindrances, etc. Throughout travel, the vehicle’s learning rate and the surrounding environment’s familiarity with it are classified. The learning neurons are connected to the information actuated and sensed by the AV to identify any unsafe vehicle activity in unknown or unidentified scenarios. Based on the risk and driving parameters, the safe and unsafe activity of the vehicles is categorised with a precise learning rate. Therefore, minor changes in vehicular decisions are monitored, and driving control is optimised accordingly to retain 7.93% of navigation assistance through a 9.76% high learning rate for different intervals.

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Ensuring Driving and Road Safety of Autonomous Vehicles Using a Control Optimiser Interaction Framework Through Smart “Thing” Information Sensing and Actuation

Almutairi,

Asmari,

Alqubaysi

et al. 2024

Machines

View full text Add to dashboard Cite

show abstract

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Robust autonomous driving control using deep hybrid-learning network under rainy/snown conditions

Cited by 1 publication

References 26 publications

Ensuring Driving and Road Safety of Autonomous Vehicles Using a Control Optimiser Interaction Framework Through Smart “Thing” Information Sensing and Actuation

Ensuring Driving and Road Safety of Autonomous Vehicles Using a Control Optimiser Interaction Framework Through Smart “Thing” Information Sensing and Actuation

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