Self-driving vehicles are regarded as the future of transportation. In near future, self-driving vehicles could ferry passengers from place to 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 an equal amount of skepticism and concern. Many people believe that self-driving vehicles will be “no safer” than human-controlled vehicles. Therefore, 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 improve self-driving car modeling using data analysis; however, an incremental approach has to be taken for in-depth exploration of data analysis techniques applied to self-driving vehicles. This is due to the lack of information regarding how rare traffic and weather events should be modeled in transportation systems.

This 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.

Potential topics include but are not limited to the following:

• Data analysis technologies for self-driving vehicles in intelligent transport systems
• Image processing for self-driving vehicles in intelligent transport systems
• Machine learning technology for self-driving vehicles in intelligent transport systems
• Traffic safety mechanisms for self-driving vehicles in intelligent transport systems
• Prediction modeling for self-driving vehicles in intelligent transport systems
• Simulation and emulation results for self-driving vehicles in intelligent transport systems
• Development of Vehicle-to-Vehicle (V2V), Vehicle-to-Vulnerable road users (V2P), and Vehicle-to-Infrastructure (V2I) communication systems for self-driving vehicles in intelligent transport systems