Safety Co-Pilot: A System for Autonomous Vehicle to Make Decision Safer and Smarter

Lu, Jiayi; Yang, Shichun; Shi, Runwu; Chen, Yu-Yi; Cao, Yaoguang

doi:10.1109/itsc55140.2022.9922477

Cited by 5 publications

(3 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A traffic accident prevention and prediction system established based in IA through data provided by a DT [78] 2022…”

Section: A Connected and Autonomous Vehiclesmentioning

confidence: 99%

“…The solution used computer-aided design (CAD) software to model transportation drawings on 3D models. Intelligent security systems have also been proposed by Lu et al [78] to create virtual entities that act as co-pilot systems. Solutions of this type are designed to make the real-time evaluation, response, and recording of failures possible.…”

Section: A Connected and Autonomous Vehiclesmentioning

confidence: 99%

See 1 more Smart Citation

Sustainable Intelligent Transportation Systems via Digital Twins: A Contextualized Survey

Martínez,

Campelo,

Ribeiro

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…A traffic accident prevention and prediction system established based in IA through data provided by a DT [78] 2022…”

Section: A Connected and Autonomous Vehiclesmentioning

confidence: 99%

Section: A Connected and Autonomous Vehiclesmentioning

confidence: 99%

Sustainable Intelligent Transportation Systems via Digital Twins: A Contextualized Survey

Martínez,

Campelo,

Ribeiro

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…However, the number of traffic accidents caused by driver violations or improper vehicle handling on the roads has not shown a significant decline [2]. While advanced driver assistance systems (ADASs) and other autonomous technologies can mitigate the risk of collisions to some extent, they cannot entirely compensate for the deficiencies in environmental perception and behavioral execution exhibited by some drivers [3,4].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Chinese Driver Road Performance Assessment Model (RPAM) for Future Licensing Examinations

Gong,

Zhang,

Liu

et al. 2023

Applied Sciences

Self Cite

View full text Add to dashboard Cite

As the demand for private vehicles rises, there has been a gradual increase in the number of motor vehicles on the roads, leading to a growing concern about addressing traffic safety. Currently, China’s approach to assessing driver capabilities remains rooted in traditional, non-intelligent, and standardized evaluation methods based on examination subjects. The traditional model often falls short in providing constructive feedback on a driver’s real-world vehicle handling abilities, as many of the examination subjects can be practiced in advance to achieve a mere passing result, which, undoubtedly, increases the likelihood of underqualified drivers on the road. To address the issues of the current examination-oriented driver evaluation system in China, we propose a road performance assessment model (RPAM) that assesses drivers comprehensively by evaluating their road environment perception and vehicle operation abilities based on an in-vehicle and out-vehicle perception system. The model leverages patterns of the driver’s head posture, along with real-time information on the vehicle’s behavior and the road conditions, to quantify various performance metrics related to reasonable operation processes. These metrics are then integrated to generate a holistic assessment of the driving capabilities. This paper ultimately conducted tests of the RPAM on one actual examination route in Beijing. Two drivers were randomly selected for the examination. The model successfully computed the overall ability scores for each driver, validating the effectiveness.

show abstract

Guardian X: Smart Copilot for Real-time Automobile accidents detection

Perumal,

Reddy,

Akash

et al. 2024

2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN)

View full text Add to dashboard Cite

Safety Co-Pilot: A System for Autonomous Vehicle to Make Decision Safer and Smarter

Cited by 5 publications

References 46 publications

Sustainable Intelligent Transportation Systems via Digital Twins: A Contextualized Survey

Sustainable Intelligent Transportation Systems via Digital Twins: A Contextualized Survey

An Intelligent Chinese Driver Road Performance Assessment Model (RPAM) for Future Licensing Examinations

Guardian X: Smart Copilot for Real-time Automobile accidents detection

Contact Info

Product

Resources

About