Special Issue on Deep Understanding of Big Geospatial Data for Self-Driving Cars
摘要截稿:
全文截稿: 2019-12-31
影响因子: 4.438
期刊难度:
CCF分类: C类
中科院JCR分区:
• 大类 : 计算机科学 - 2区
• 小类 : 计算机:人工智能 - 2区
Overview
A self-driving car means that a car is capable of sensing its environment and moving with little or no human input. Compared to traditional human-driving cars, self-driving cars have the potential to reduce traffic accidents, traffic congestions, and fuel consumption. There is no doubt that the self-driving is the future direction of intelligent transportation.
Big geospatial data understanding plays a fundamental role in self-driving cars, 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, geospatial 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 geospatial data face many challenges. In this special issue, we invite researchers to address the challenges on deep understanding of big geospatial data for self-driving cars.
The list of possible topics include, but not limited to:
Deep understanding of big geospatial data
Geospatial data preprocessing, including data cleaning, feature selection and extraction, data clustering, and map-matching.
Distributed and parallel computing for big geospatial data
Deep learning/reinforcement learning/federated learning on big geospatial data
Big geospatial data mining
Geospatial data driven self-driving applications
Deep understanding of traffic satellite images
Driving behavior analytics and prediction
Traffic flow/human mobility detection and prediction