Automotive Doppler sensing: The Doppler profile with machine learning in vehicle-to-vehicle networks for road safety

Kihei, Billy; Copeland, John A.; Chang, Yusun

doi:10.1109/spawc.2017.8227637

Cited by 17 publications

(9 citation statements)

References 7 publications

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“…As is the case in other domains [404], support for closed-loop settings optimisation and including an accelerator physics point of view towards controls will help to improve the efficiency of the accelerator complex [405]. This will become possible with the higher computing power and data exchange capacities, more flexible analysis using "Big Data" approaches, the introduction of machine learning, model driven approaches and an end-to-end cost/benefit sensitivity analysis.…”

Section: Controls Requirements and Conceptsmentioning

confidence: 99%

FCC-ee: The Lepton Collider

Abada¹,

Abbrescia²,

AbdusSalam³

et al. 2019

Eur. Phys. J. Spec. Top.

677

511

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Section: Controls Requirements and Conceptsmentioning

confidence: 99%

FCC-ee: The Lepton Collider

Abada¹,

Abbrescia²,

AbdusSalam³

et al. 2019

Eur. Phys. J. Spec. Top.

677

511

View full text Add to dashboard Cite

show abstract

“…Considering some specificities of autonomous truck and its risks, at least a few more studies about the topic could be expected. [64], [67], [70], [61], [65], [69], [62], [58], [63], [71], [72], [74], [68], [ [39], [18], [32], [31], [33], [26], [28], [30], [55], [52], [29], [ [75], [41], [29], [20], [44], [35], Prediction of adc vanced driver assistance systems (ADAS) remaining useful life (RUL) for the prognosis of ADAS safety critical components Pedestrian Detection; How to "automate" manual annotation for images to train visual perception for AVs Road junction detection; [52], [27], [37], [30] Bayesian Artificial Intelligence…”

Section: Final Remarksmentioning

confidence: 99%

“…Road Detection; Road environmental recognition and various object detection in real driving conditions; How to "automate" manual annotation for images to train visual perception for AVs; [60], [38], [20] Optimiza-tion Heuristics 3 5%…”

Section: %mentioning

confidence: 99%

“…No experimental results were presented. [20] Road Detection Road area detection method using a support vector machine (SVM) and histogram of oriented gradient (HOG) features and 3D lidar Support Vector Machine (SVM) combined to other techniques Classifier to differentiate road areas from other areas using a 3D Lidar with machine larning techniques. Range data of lidar changes in the layer direction but not in the rotational direction.…”

Section: Support Vectormentioning

confidence: 99%

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A Systematic Literature Review About the Impact of Artificial Intelligence on Autonomous Vehicle Safety

Nascimento

Vismari

Molina

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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Autonomous Vehicles (AV) are expected to bring considerable benefits to society, such as traffic optimization and accidents reduction. They rely heavily on advances in many Artificial Intelligence (AI) approaches and techniques. However, while some researchers in this field believe AI is the core element to enhance safety, others believe AI imposes new challenges to assure the safety of these new AI-based systems and applications. In this non-convergent context, this paper presents a systematic literature review to paint a clear picture of the state of the art of the literature in AI on AV safety. Based on an initial sample of 4870 retrieved papers, 59 studies were selected as the result of the selection criteria detailed in the paper. The shortlisted studies were then mapped into six categories to answer the proposed research questions. An AV system model was proposed and applied to orient the discussions about the SLR findings. As a main result, we have reinforced our preliminary observation about the necessity of considering a serious safety agenda for the future studies on AIbased AV systems.

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“…Vehicular Ad-Hoc Networks are made of a group of, mostly, mobile vehicles. Contextual awareness has been used in vehicle ad-hoc networks for a variety of applications including route planning and learning [53]- [55], real time perception of traffic congestion [56], collision avoidance [57] and finding vacant carparks [58]. In the maritime domain contextual awareness systems have even been used to detect anomalous behaviour in order to predict security threats [59].…”

Section: A Contextual Awareness In Vehicular Ad-hoc Networkmentioning

confidence: 99%

Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey

et al. 2019

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Autonomous vehicles are becoming a reality in places with advanced infrastructure to support their operations. In crowded places, harsh environments, missions that require these vehicles to be aware of the context in which they are operating, and situations requiring continuous coordination with humans such as in disaster relief, advanced-vehicle systems (AVSs) need to be better contextually aware. The vast literature referring to ''context-aware systems'' is still sparse, focusing on very limited forms of contextual awareness. It requires a structured approach to bring it together to truly realize contextual awareness in AVSs. This paper uses a human-AVSs (HAVSs) lens to polarize the literature in a coherent form suitable for designing distributed HAVSs. We group the relevant literature into two categories: contextual-awareness related to the vehicle infrastructure itself that enables AVSs to operate, and contextual-awareness related to HAVSs. The former category focuses on the communication backbone for AVSs including ad-hoc networks, services, wireless communication, radio systems, and the cyber security and privacy challenges that arise in these contexts. The latter category covers recommender systems, which are used to coordinate the actions that sit at the interface of the human and AVSs, human-machine interaction issues, and the activity recognition systems as the enabling technology for recommender systems to operate autonomously. The structured analysis of the literature has identified a number of open research questions and opportunities for further research in this area.

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Automotive Doppler sensing: The Doppler profile with machine learning in vehicle-to-vehicle networks for road safety

Cited by 17 publications

References 7 publications

FCC-ee: The Lepton Collider

FCC-ee: The Lepton Collider

A Systematic Literature Review About the Impact of Artificial Intelligence on Autonomous Vehicle Safety

Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey

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