Intelligent transportation systems (ITSs) play an important role in emerging smart cities (SCs), improving the time and energy efficiency of transportation in the cities. A key enabler of the ITS is autonomous vehicle (AV) that is equipped with communication and computing capabilities. The AVs are also empowered by big data analytics and artificial intelligence (AI) and can quickly react and adapt to the changing road conditions of SCs. This chapter first describes the characteristics of big data in an SC, and vehicular mobility models based on big data analytics. Two examples of big-data-driven intelligent management of AVs are provided. Then, a network calculus (NC)-based fleet management method is presented to improve the energy efficiency of AVs and meanwhile offers passengers the best possible experience. At last, a federated learning (FL)-based autonomous driving framework is described to achieve privacy-preserving, intelligent management of the AVs in emerging SCs.

Chapter Contents:

• 9.1 Motivation and introduction
• 9.2 Big data analytic and vehicular mobility modeling for smart city
• 9.2.1 Description of captured city data
• 9.2.2 Vehicular mobility models based on data analysis
• 9.2.2.1 Vehicular traffic flow models
• 9.2.2.2 Driving behavior models
• 9.3 Network calculus-assisted intelligent management of autonomous vehicle fleet in smart city
• 9.3.1 Constructing a resource model through ML
• 9.3.1.1 Resource model under ML
• 9.3.1.2 Road system under network calculus
• 9.3.1.3 Minimizing waiting time by matching optimization
• 9.3.2 Online traffic modeling and management
• 9.4 Federated-learning-based autonomous driving for secure intelligent AVs management
• 9.4.1 Background
• 9.4.2 FL-based autonomous driving structure
• 9.4.3 Performance analysis
• 9.5 Conclusion
• References

Inspec keywords: transportation; artificial intelligence; Big Data; intelligent transportation systems; road vehicles; data privacy

Other keywords: artificial intelligence; network calculus-based fleet management method; federated learning-based autonomous driving framework; smart cities; intelligent transportation systems; Big-data-driven intelligent management; AV; autonomous vehicle; FL-based autonomous driving framework

Subjects: Data security; Knowledge based systems; Other DBMS; Traffic engineering computing