A Survey of the Connected Vehicle Landscape—Architectures, Enabling Technologies, Applications, and Development Areas
By Joshua E. Siegel, Dylan C. Erb, Sanjay E. Sarma. IEEE transactions on intelligent transportation systems (Print), 2017
390 citations recorded by the source corpus.
This paper summarizes the state of the art in connected vehicles—from the need for vehicle data and applications thereof, to enabling technologies, challenges, and identified opportunities. Connectivity is increasing around the world and its expansion to vehicles is no exception. With improvements in connectivity, sensing, and computation, the future will see vehicles used as development platforms capable of generating rich data, acting based on inference, and effecting great change in transportation, the human-vehicle dynamic, the environment, and the economy. Connected vehicle technologies have already been used to improve fleet safety and efficiency, with emerging technologies additionally allowing data to be used to inform aspects of vehicle design, ownership, and use. While the demand for connected vehicles and its enabling technology has progressed significantly in recent years, there remain challenges to connected and collaborative vehicle application deployment before the full potential of connected cars may be realized. From extensibility and scalability to privacy and security, this paper informs the reader about key enabling technologies, opportunities, and challenges in the connected vehicle landscape.
Subfields: Automotive Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/32f881dc5bfbffc053beb8a7ced09d7fa96e7533
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In Automotive Engineering
In Electrical and Electronic Engineering
- Fan Liu
- Khaled Ben Letaief
- Christos Masouros
- Derrick Wing Kwan Ng
- Jun Zhang
- Weijie Yuan
- Celimuge Wu
- Yusheng Ji
Citations in the corpus
- Integrated Sensing and Communication-Assisted Orthogonal Time Frequency Space Transmission for Vehicular Networks (2021, background): “…it is expected that the vehicular communication will play an increasingly important role in people’s daily lives, as it can support various promising applications, e.g., autonomous driving, traffic management, and on-the-go Internet services in intelligent transportation systems (ITS) [1]–[3].”
- Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues (2020, background): “veyed in [105] where technical challenges and opportunities are also discussed.”
- Mobile Edge Intelligence and Computing for the Internet of Vehicles (2019, background): “Connecting vehicles via Vehicular Ad-hoc Networks (VANET) represents an early attempt to support safety-related applications, such as accident warning, crash notification, and cooperative cruise control [4], [5].”
- Research Advances and Challenges of Autonomous and Connected Ground Vehicles (2019, background): “With vehicle connectivity, the perception layer can share its perception data with other road users, and the planning layer is able to perform cooperative driving with other road users [53], [54].”
- Bayesian Predictive Beamforming for Vehicular Networks: A Low-Overhead Joint Radar-Communication Approach (2020, background): “Having the estimates of delay and Doppler, we have the received signal samples y k,n = [ y [1] k,n , ..., y [ N r ] k,n ] T for θ k,n and β k,n based on the filtered signal, given by where the term z k,n = [ z [1] k,n , ..., z [ N r ] k,n ] T denotes the noise samples at different receive antennas.”
- Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys (2023, neutral): “ware [97], communication [98], [99], [100], [101], [102], [103], [104], [105], [106], [107], [108], [109], [110], [111], [112],”