Data Analysis for Self-Driving Vehicles in Intelligent Transportation Systems

Self-driving vehicles are regarded as the future of transportation. In the near future, self-driving vehicles would ferry passengers from one place to another place, like driverless taxis, and transport packages and raw materials from city to city. However, for all the optimism surrounding self-driving vehicles, there is also an equal amount of scepticism and concern. Many people believe that self-driving vehicles will be “no safer” than human-controlled vehicles. #erefore, the willingness of the public to ride in a fully self-driving vehicle will be very low due to nonzero accident rates. A lot more data and testing are required to influence the public’s beliefs on self-driving vehicles being ready for the road. Collecting more datasets will help to improve self-driving car modelling using data analysis; however, an incremental approach has to be taken for in-depth exploration of data analysis techniques applied to self-driving vehicles. #is is due to the lack of sufficient information regarding how rare traffic and weather events should be modelled in transportation systems. #is special issue aims to provide a comprehensive overview of the most recent and promising advancements of data analysis technologies for self-driving vehicles in intelligent transportation systems. Data analysis technologies for self-driving vehicles are expected to cover the current state of the art and highlight remaining challenges and barriers to the development of self-driving vehicles as part of intelligent transportation systems. 11 papers were submitted for this issue; 4 of them were accepted for publication. Below, we provide an overview of the selected articles for this special issue. Vehicle Movement Analyses Considering Altitude Based on Modified Digital Elevation Model and Spherical Bilinear Interpolation Model: Evidence from GPS-Equipped Taxi Data in Sanya, Zhengzhou, and Liaoyang.#emodified digital elevation (MDE) model and spherical bilinear interpolation (SBI) model were proposed for vehicle movement analyses considering altitude. In addition, the experimental data of 9,990 GPS-enabled taxis in Sanya, Zhengzhou, and Liaoyang were adopted to support comparisons. Measurement results showed that the MDEmodel having over 99% less disparity with direct solution as compared to the original model and SBI model could further improve the effects. In conclusion, the contributions of this study are as follows: (1) the MDE model was built to calculate vehicle movements by digital elevation data based on mathematical equations and (2) the SBI model was proposed and applied to improve the preciseness of GPS data with altitude of collaborative vehicles.