Evaluation of Performance Enhancement for Crash Constellation Prediction via Car-to-Car Communication

Kuehbeck, Thomas; Hakobyan, Gor; Sikora, Axel; Chibelushi, Claude C.; Moniri, M.

doi:10.1007/978-3-319-06644-8_6

Cited by 4 publications

(4 citation statements)

References 10 publications

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“…(5) Te Cumulative Acquisition Cost of Fleet Intellectualization in Beijing. Te annual acquisition costs of feet intellectualization are calculated using equation (8), and the cumulative acquisition cost in Beijing from 2023 to 2050 is determined using equation (9). Tese calculations take into account the anticipated cost reductions associated with various intelligent confgurations in the future.…”

Section: (4) Te Vehicle Sales and Market Penetration Data In Beijingmentioning

confidence: 99%

“…On the basis of fundamental autonomous driving capabilities, intelligent vehicles can replace humans in driving tasks. It not only provides passengers with a safer and more comfortable driving experience but also increases travel efciency [1][2][3][4], saves energy, and reduces emissions [5][6][7] and trafc accident rates [8][9][10]. Chinese consumers have the highest level of acceptance of self-driving technology in the world [11].…”

Section: Introductionmentioning

confidence: 99%

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Cost Analysis of Vehicle‐Road Cooperative Intelligence Solutions for High‐Level Autonomous Driving: A Beijing Case Study

Zhu,

Zhao,

Song

et al. 2024

Journal of Advanced Transportation

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The development of the vehicle-road cooperative intelligence can effectively resolve the current technical impediment and cost quandary associated with high-level autonomous driving. Nevertheless, the intelligent infrastructure entails initial deployment costs and ongoing energy consumption and maintenance costs, necessitating a comprehensive and quantitative analysis of the costs of intelligent infrastructure and the corresponding changes in comprehensive costs. The cost evaluation model for the cooperative intelligent system is designed in this paper, considering the corresponding intelligent infrastructure layout scheme for different road types within the technical framework. The intelligent configuration and corresponding cost transfer from roadside to vehicle side under the synergy effect is also analyzed. Using Beijing as a case study, the results indicate that the deployment of intelligent infrastructure will effectively reduce acquisition and usage costs of high-level intelligent vehicles and achieve a greater “reuse” effect by serving more intelligent connected vehicles (ICVs). Compared to the vehicle intelligence, collaborative intelligence will reduce cumulative total costs by more than ¥200 billion from 2023 to 2050, even with the inclusion of intelligent infrastructure’s costs.

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Section: (4) Te Vehicle Sales and Market Penetration Data In Beijingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cost Analysis of Vehicle‐Road Cooperative Intelligence Solutions for High‐Level Autonomous Driving: A Beijing Case Study

Zhu,

Zhao,

Song

et al. 2024

Journal of Advanced Transportation

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show abstract

“…Based on the core autonomous driving functions, ICVs can replace human beings to complete driving tasks. It can not only provide passengers with a safer, more comfortable, and intelligent driving experience but also improve travel efficiency [3][4][5][6], save energy and reduce emissions [7][8][9], and reduce the traffic accident rate [10][11][12]. At present, the development route of autonomous driving is gradually changing from vehicle intelligence to "vehicle to X" (V2X) [13].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Cost of Intelligent Upgrades of Transportation Infrastructure for Intelligent Connected Vehicles

Liu

Song

Tan

et al. 2022

Journal of Advanced Transportation

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Intelligent connected vehicles (ICVs) have become the focus and development direction of the automobile industry. As a flexible intelligent terminal, ICVs will become a necessary part of the intelligent transportation system. The routes of developing ICVs based on “vehicle to X” (V2X) can effectively alleviate the demands of vehicles for intelligent functions and cut related research costs, accelerating commercialization of ICVs and leading to many social benefits. At present, China has made it clear to develop ICVs based on V2X, which requires simultaneous intelligent upgrades of vehicles and transportation infrastructure. Therefore, intelligent upgrades of transportation infrastructure must match the functional requirements of ICVs. In addition, the investment in intelligent upgrades of transportation infrastructure is mainly from the government, so the costs must be controlled reasonably to find the most cost-effective upgrade route. In this paper, the types of intelligent transportation infrastructures were determined by sorting out the demands of ICVs for transportation infrastructure, and the deployment methods and upgrade routes of intelligent transportation infrastructures were designed. Then, the cost evaluation model for intelligent upgrade of transportation infrastructures was established, based on which, the cost evaluation of different intelligent upgrade routes of transportation infrastructure was carried out in closed highway and open urban road scenarios to determine the optimal route. Besides, the key elements affecting the cost of transportation infrastructure upgrades were identified, and their impact degrees on transportation infrastructure upgraded were analyzed by scenario analysis. The results show that the intelligent transportation infrastructure for advanced ICVs mainly includes communication base stations, roadside units (RSUs), vision sensors, millimeter-wave radars, laser radars (LiDARs), meteorological sensors, intelligent signal machines, edge computing servers, and cloud computing centers. The route of deploying primary intelligent transportation infrastructure at first and then directly upgrading them to advanced level can well match the functional requirements of ICVs on the basis of lower costs. The costs of RSUs, LIDARS, and edge computing servers as well as data transmission rate of 5G are key elements affecting the costs of intelligent upgrades of transportation infrastructure.

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“…An investigation conducted by (Szczęsna et al 2016) on the car assessment dataset utilises different information mining strategies to explore the quality execution of different classifiers. (Castro et al 2020; Jain and Kr 2017; Kuehbeck et al 2014), In this work, different sorts of information mining classification strategies to the car assessment dataset are connected (Edgar 2020). The strategy made with the preparing dataset has been assessed with the standard measurements such as precision, review and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Automotive Vehicles Quality Prediction based on Features Customization and Differentiators using Artificial Neural Network in Comparison with Digraph Approach

2022

pnr

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The aim of the proposed work is to predict the performance of an Artificial Neural Network (ANN) algorithm in detection of vehicle quality performance based on Features customization and differentiators by comparing it with the Digraph algorithm. Materials and Methods:The proposed ANN is trained and tested with a "Novel Car Evaluation Database" created by Marko Bohanec. With correct quality 826 samples and inaccurate quality 826 samples in two groups with a total sample size of 1652. Training data [75% of dataset] and testing data set [25% of data set] are separated from the obtained samples.The samples are calculated using G power analysis using clincalc, which includes two groups: alpha (0.05), power (80%). There is a statistically significant difference between the groups with p=0.04 Results: The proposed ANN algorithm provided better results in predicting the quality of novel car evaluation compared to the Digraph approach. Conclusion: For the given data set, from the results it is found that the ANN algorithm is significantly more suitable for recognition of better car quality.

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Evaluation of Performance Enhancement for Crash Constellation Prediction via Car-to-Car Communication

Cited by 4 publications

References 10 publications

Cost Analysis of Vehicle‐Road Cooperative Intelligence Solutions for High‐Level Autonomous Driving: A Beijing Case Study

Cost Analysis of Vehicle‐Road Cooperative Intelligence Solutions for High‐Level Autonomous Driving: A Beijing Case Study

Evaluation of the Cost of Intelligent Upgrades of Transportation Infrastructure for Intelligent Connected Vehicles

Automotive Vehicles Quality Prediction based on Features Customization and Differentiators using Artificial Neural Network in Comparison with Digraph Approach

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