Study on mixed traffic of autonomous vehicles and human-driven vehicles with different cyber interaction approaches

Guo, Xin-Yue; Zhang, Geng; Jia, Ai-Fang

doi:10.1016/j.vehcom.2022.100550

Cited by 17 publications

(4 citation statements)

References 37 publications

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“…However, there are a few drawbacks in RF-based ITS (or VANET) including (i) inter ference-due to sharing the RF carrier among the other systems/services [361]; (ii) high installation costs-due to the installation of a DSRC link with the mounting of an OBU and RSU on every vehicle and roadside supporting post/station, respectively; (iii) incompatibility with systems/services of future revolutions; (iv) packet collision/broadcast storm-due to the simultaneous transmission of data from multiple vehicles [362]; and (v) environmental/human hazards-due to the nearest high RF power radiation from/to vehicles [363]. To overcome these issue, one possible option would be to adopt the VLC-OCC technology [364], where LED (or laser diode)-based front, back, and sides lights with a camera can be used to detect an object/people, warning of possible collisions, enabling driver safety, estimating the range and data communications among vehicles, service stations, and vehicles to/from service stations [348,358].…”

Section: Optical Camera Communication (Occ)mentioning

confidence: 99%

A Review–Unguided Optical Communications: Developments, Technology Evolution, and Challenges

et al. 2023

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This review paper discusses the complete evolution of free-space optical (FSO) communication, also known as unguided optical communication (UOC) technologies, all the way back to ancient man’s fire to today’s machine-learning-supported UOC systems. The principles, significance, and developments that have happened over the past several decades, as well as installation methodologies, technological limitations, and today’s challenges of UOCs are presented. All the subsets of UOC: FSO communication, underwater optical wireless communication (UOWC), and visible light communication (VLC), with their technology/system developments, potential applications, and limitations are reviewed. The state-of-the-art developments/achievements in (i) FSO channel effects and their mitigation techniques; (ii) radio-over-FSO techniques; (iii) wavelength division multiplexing and sub-carrier multiplexing techniques; (iv) FSO for worldwide interoperability for microwave access applications; (v) space optical satellite communication (SOSC); (vi) UWOC; (vii) photoacoustic communication (PAC); (viii) light-fidelity; (ix) VLC; (x) vehicular VLC (V2LC); and (xi) optical camera communication are reviewed. In addition, the current developments on emerging technologies such as artificial intelligence (to improve the performance of UOC systems), energy harvesting (for the effective utilization of UOC channels), and near-future communication network scenarios (mandatory for secured broadband digital links) are covered. Finally, in brief, to achieve the full potential of UOC systems, challenges that require immediate research attention are summarized.

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Section: Optical Camera Communication (Occ)mentioning

confidence: 99%

A Review–Unguided Optical Communications: Developments, Technology Evolution, and Challenges

et al. 2023

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“…In the proposed traffic model, an appropriate strategy would be quantified for the cooperative (C) vehicles and defective (D) vehicles to ensure a maximum social benefit from under dilemma environment. To maximize the social payoff, several kinds of CA models [1][2][3][4][5] have been studied considering various phenomena such as cluster or platoons car following model [6][7][8][9][10][11][12][13], human-automated mixed traffic model [14][15][16][17][18], route section traffic model following the traffic forecasting [19][20][21][22][23][24][25], and some models introduced advanced lane changing protocol based on the preceding vehicle or vehicle cluster [26][27][28]. These models didn't pay attention to the following car or vehicle cluster, which is an influential factor in enhancing the social benefits.…”

Section: Introductionmentioning

confidence: 99%

A dilemma study of the traffic flow system emerged due to the lane-change by follower’s tailgating effect

Sueyoshi,

Hossain,

Tanimoto

2024

Preprint

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In this study, a virtual traffic system has been developed by establishing a brand-new Cellular Automata (CA) traffic model to figure out adequate strategies that can be implemented in actual traffic systems to optimize road capability. We investigate the flow efficiency and social dilemma that emerged due to the defector drivers in a traffic flow system, who are highly aggressive in driving and impose threatening/pushing effects on their preceding while they are tailgating. The evolutionary game theory, which is one of the most efficient tools in the decision-making process, has been utilized to identify the Social Efficiency Deficit (SED), which means the dilemma strength of those games. We introduced a new lane-changing protocol for the preceding vehicle, considering the threatening effects given by the aggressive follower. This investigation explored several case studies defining various strategies for cofactors and defectors. We conducted a series of multi-agent simulations on this traffic flow system and experienced the Prisoner’s Dilemma (PD) and the Quasi-Prisoner’s Dilemma game with diverse dilemma strengths for four different strategies for cooperators and defectors.

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“…Emphasizing the societal implications [9], leveraging predictive analytics transcends academic pursuits; while the complexity of accident prediction has been elaborated upon, the real-world gains are significant. Harnessing data-driven insights through modern machine learning (ML) techniques [10], such as DL and AI, is not just a technological leap; it is a step towards safer roadways, economic efficiency, and, most importantly, the preservation of human life.…”

Section: Introduction and Related Workmentioning

confidence: 99%

LLM Multimodal Traffic Accident Forecasting

de Zarzà,

de Curtò,

Roig

et al. 2023

Sensors

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With the rise in traffic congestion in urban centers, predicting accidents has become paramount for city planning and public safety. This work comprehensively studied the efficacy of modern deep learning (DL) methods in forecasting traffic accidents and enhancing Level-4 and Level-5 (L-4 and L-5) driving assistants with actionable visual and language cues. Using a rich dataset detailing accident occurrences, we juxtaposed the Transformer model against traditional time series models like ARIMA and the more recent Prophet model. Additionally, through detailed analysis, we delved deep into feature importance using principal component analysis (PCA) loadings, uncovering key factors contributing to accidents. We introduce the idea of using real-time interventions with large language models (LLMs) in autonomous driving with the use of lightweight compact LLMs like LLaMA-2 and Zephyr-7b-α. Our exploration extends to the realm of multimodality, through the use of Large Language-and-Vision Assistant (LLaVA)—a bridge between visual and linguistic cues by means of a Visual Language Model (VLM)—in conjunction with deep probabilistic reasoning, enhancing the real-time responsiveness of autonomous driving systems. In this study, we elucidate the advantages of employing large multimodal models within DL and deep probabilistic programming for enhancing the performance and usability of time series forecasting and feature weight importance, particularly in a self-driving scenario. This work paves the way for safer, smarter cities, underpinned by data-driven decision making.

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Study on mixed traffic of autonomous vehicles and human-driven vehicles with different cyber interaction approaches

Cited by 17 publications

References 37 publications

A Review–Unguided Optical Communications: Developments, Technology Evolution, and Challenges

A Review–Unguided Optical Communications: Developments, Technology Evolution, and Challenges

A dilemma study of the traffic flow system emerged due to the lane-change by follower’s tailgating effect

LLM Multimodal Traffic Accident Forecasting

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