Topical Collection on Deep Understanding of Big Geo-Social Data for Autonomous Vehicles
Autonomous vehicles mean that vehicles are capable of sensing their environment and moving with little or no human input. Compared to traditional human-driving cars, autonomous vehicles have the potential to reduce traffic accidents, traffic congestions, and fuel consumption. There is no doubt that the autonomous-driving is the future direction of intelligent transportation.
Big geo-social data understanding plays a fundamental role in autonomous vehicles, which is helpful in acquiring the patterns of driving/travel behavior, human mobility, and traffic flow, and in sensing the environment and giving a traffic-aware navigation. Generally, geo-social data include road network data, digital elevation model (DEM) data, vehicle and human trajectory data, traffic flow data, traffic accident data, traffic satellite image data, and location-based social media data. The storage and deep understanding of geo-social data face many challenges. In this special issue, we invite researchers to address the challenges on deep understanding of big geo-social data for autonomous vehicles.
The list of possible topics include, but not limited to:
- Deep understanding of big geo-social data
- Geo-social data preprocessing, including data cleaning, feature selection and extraction, data clustering, and map-matching
- Deep learning/reinforcement learning/federated learning on big geo-social data
- Big geo-social data mining
- Geo-social data driven autonomous vehicle applications
- Deep understanding of traffic satellite images
- Driving behavior analytics and prediction
- Traffic flow/human mobility detection and prediction
- Traffic-aware routing and navigation
- Geo-social Crowdsourcing for autonomous vehicles
Guest Editorial Team
Shuo Shang (lead Guest Editor), University of Electronics Science and Technology of China, China, firstname.lastname@example.org
Jianbing Shen, Inception Institute of Artificial Intelligence, UAE, email@example.com
Jirong Wen, Renmin University of China, China, firstname.lastname@example.org
Panos Kalnis, King Abdullah University of Science and Technology, Saudi Arabia, email@example.com
Paper submission deadline: extended to March 31, 2021
First notification: May 31, 2021
Revision: July 28, 2021
Final decision: September 30, 2021
Peer Review Process
All the papers will go through a double blind review process and will be reviewed by at least two reviewers. A thorough check will be done and the guest editors will check any significant similarity between the manuscript under consideration and any published paper or submitted manuscripts of which they are aware. In such case, the article will be directly rejected without proceeding further. Guest editors will make all reasonable effort to receive the reviewer’s comments and recommendation on time.
The submitted papers must provide original research that has not been published nor currently under review by other venues. Previously published conference papers should be clearly identified by the authors at the submission stage and an explanation should be provided about how such papers have been extended. At least 30% of new content is expected.
Paper submissions for the special issue should follow the submission format and guidelines (https://www.springer.com/journal/521/submission-guidelines). Each manuscript should not exceed 16 pages in length (inclusive of figures and tables).
Authors should select ‘SI: Deep Understanding of Big Geo-Social Data for Autonomous Vehicles' during the submission step 'Additional Information'.