Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Laura Li

dblp:252/1049 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Vehicular, aerial and satellite networks · 87% Edge and fog computing · 13%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Vehicular, aerial and satellite networks › connected vehicles
connected autonomous vehicles
0.412019
Cooperative Connected Autonomous Vehicles (CAV): Research, Applications and Challenges · ICNP 2019
Vehicular, aerial and satellite networks
intelligent transportation systems
0.412019
Cooperative Connected Autonomous Vehicles (CAV): Research, Applications and Challenges · ICNP 2019
YearPublicationVenuePosition
2019 Cooperative Connected Autonomous Vehicles (CAV): Research, Applications and Challenges
abstract
Road accidents and traffic congestion are two critical problems for global transport systems. Connected vehicles (CV) and automated vehicles (AV) are among the most heavily researched and promising automotive technologies to reduce road accidents and improve road efficiency. However, both AV and CV technologies have inherent shortcomings, for example, line of sight sensing limitation of AV sensors and the dependency of high penetration rate for CVs. In this paper we present a cooperative connected intelligent vehicles (CAV) framework. It is motivated by the observation that vehicles are increasingly intelligent with various levels of autonomous functionalities. The vehicles intelligence is boosted by more sensing and computing resources. These sensor and computing resources of CAV vehicles and the transport infrastructure could be shared and exploited. With resource sharing and cooperation CAVs can have comprehensive perception of driving environments, and novel cooperative applications can be developed to improve road safety and efficiency (RSE). The key feature of the cooperative CAV system is the cooperation within and across the key players in the road transport systems and across system layers. For example, the various levels of cooperation include cooperative sensing, cooperative RSE applications and cooperation among the vehicles and among the vehicles and infrastructure. We will present the potentials that could be brought by cooperative CAV, the roadmap for research and development, the preliminary research results and open issues.
Jianhua He 0001, Andrew Radford, Laura Li, Zhiliang Xiong, Zuoyin Tang, Xiaoming Fu 0001, Supeng Leng, Fan Wu 0012, Kaisheng Huang, Jianye Huang 0003, Jie Zhang 0003, Yan Zhang 0002
ICNP3