A Review and Outlook on Predictive Cruise Control of Vehicles and Typical Applications Under Cloud Control System

Gao, Bolin; Wan, Keke; Chen, Qien; Wang, Zhou; Li, Rui; Yu, Junsheng; Mei, Run; Luo, Yinghui; Li, Keqiang

doi:10.1007/s11633-022-1395-3

Cited by 5 publications

(3 citation statements)

References 138 publications

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“…Khanal et al (2023) utilized CV data to develop crash prediction models. Gao et al (2023) provided insights into predictive cruise control under cloud control systems, emphasizing the role of predictive algorithms in traffic efficiency and safety [17]. Islam and Abdel-Aty (2023) focused on using CV data for traffic conflict prediction [18].…”

Section: Connected Vehicles Trendsmentioning

confidence: 99%

Alignment of Unsupervised Machine Learning with Human Understanding: A Case Study of Connected Vehicle Patents

Bridgelall

2024

Applied Sciences

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As official public records of inventions, patents provide an understanding of technological trends across the competitive landscape of various industries. However, traditional manual analysis methods have become increasingly inadequate due to the rapid expansion of patent information and its unstructured nature. This paper contributes an original approach to enhance the understanding of patent data, with connected vehicle (CV) patents serving as the case study. Using free, open-source natural language processing (NLP) libraries, the author introduces a novel metric to quantify the alignment of classifications by a subject matter expert (SME) and using machine learning (ML) methods. The metric is a composite index that includes a purity factor, evaluating the average ML conformity across SME classifications, and a dispersion factor, assessing the distribution of ML assigned topics across these classifications. This dual-factor approach, labeled the H-index, quantifies the alignment of ML models with SME understanding in the range of zero to unity. The workflow utilizes an exhaustive combination of state-of-the-art tokenizers, normalizers, vectorizers, and topic modelers to identify the best NLP pipeline for ML model optimization. The study offers manifold visualizations to provide an intuitive understanding of the areas where ML models align or diverge from SME classifications. The H-indices reveal that although ML models demonstrate considerable promise in patent analysis, the need for further advancements remain, especially in the domain of patent analysis.

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Section: Connected Vehicles Trendsmentioning

confidence: 99%

Alignment of Unsupervised Machine Learning with Human Understanding: A Case Study of Connected Vehicle Patents

Bridgelall

2024

Applied Sciences

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“…Khanal et al (2023) used CV data to develop crash prediction models for intersections and road segments, offering a proactive approach to road safety [21]. Gao et al (2023) provided a review and outlook on predictive cruise control of vehicles under cloud control systems, emphasizing the role of predictive algorithms in enhancing traffic efficiency and safety [22]. Islam and Abdel-Aty (2023) used CV data to predict traffic conflicts [23].…”

Section: Road Safetymentioning

confidence: 99%

A Systematic Patent Review of Connected Vehicle Technology Trends

Bridgelall

2024

Future Transportation

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Governments and manufacturers anticipate that connected vehicle deployments will reduce accidents, optimize traffic flow, and enhance the driving experience. Although extensive studies focus on the technical aspects of connected vehicles, a gap exists in systematically analyzing the invention trends shaping the field. Insights into these trends is a strategic imperative for policymakers, researchers, and investors alike. This study presents a systematic patent review (SPR) as a robust and adaptable methodological framework for patent analysis. Adapted from the established systematic literature review (SLR), the SPR offers detailed insights into both the thematic and temporal trajectories of innovation in any technology field. The SPR identifies 220 U.S. patents from 2018 to 2022 and classifies them into specific objective categories such as computing resources, cybersecurity, and driving safety, among others. The study notes an increasing focus on driving safety and secure wireless communications, which aligns with broader goals of enhancing safety and situational awareness in transportation. Both the methodological framework and findings address an existing gap in the literature, guide future research, policymaking, and investment in the field of connected vehicles.

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“…For example, ref. [20] proposed a machine learning-based algorithm for vehicle routing optimization, which achieved significant reductions in fuel consumption and travel time. In another study, ref.…”

Section: Urban Traffic Management Strategiesmentioning

confidence: 99%

Enhancing Energy Efficiency in Connected Vehicles for Traffic Flow Optimization

Shahbazi,

Nowaczyk

2023

Smart Cities

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In urban settings, the prevalence of traffic lights often leads to fluctuations in traffic patterns and increased energy utilization among vehicles. Recognizing this challenge, this research addresses the adverse effects of traffic lights on the energy efficiency of electric vehicles (EVs) through the introduction of a Multi-Intersections-Based Eco-Approach and Departure strategy (M-EAD). This innovative strategy is designed to enhance various aspects of urban mobility, including vehicle energy efficiency, traffic flow optimization, and battery longevity, all while ensuring a satisfactory driving experience. The M-EAD strategy unfolds in two distinct stages: First, it optimizes eco-friendly green signal windows at traffic lights, with a primary focus on minimizing travel delays by solving the shortest path problem. Subsequently, it employs a receding horizon framework and leverages an iterative dynamic programming algorithm to refine speed trajectories. The overarching objective is to curtail energy consumption and reduce battery wear by identifying the optimal speed trajectory for EVs in urban environments. Furthermore, the research substantiates the real-world efficacy of this approach through on-road vehicle tests, attesting to its viability and practicality in actual road scenarios. In the proposed case, the simulation results showcase notable achievements, with energy consumption reduced by 0.92% and battery wear minimized to a mere 0.0017%. This research, driven by the pressing issue of urban traffic energy efficiency, not only presents a solution in the form of the M-EAD strategy but also contributes to the fields of sustainable urban mobility and EV performance optimization. By tackling the challenges posed by traffic lights, this work offers valuable insights and practical implications for improving the sustainability and efficiency of urban transportation systems.

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A Review and Outlook on Predictive Cruise Control of Vehicles and Typical Applications Under Cloud Control System

Cited by 5 publications

References 138 publications

Alignment of Unsupervised Machine Learning with Human Understanding: A Case Study of Connected Vehicle Patents

Alignment of Unsupervised Machine Learning with Human Understanding: A Case Study of Connected Vehicle Patents

A Systematic Patent Review of Connected Vehicle Technology Trends

Enhancing Energy Efficiency in Connected Vehicles for Traffic Flow Optimization

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