Autonomous Driving Cars for Smart Cities
▶ Introduction
 

The recent proliferation of miniaturized autonomous driving technologies has revolutionized cities by making smart cars a viable option for daily transportation. Autonomous cars alleviate human drivers’ burden by performing intelligent operations, such as collision avoidance, lane departure warning, and traffic sign detection. In addition, autonomous driving technologies can efficiently manage traffic flow and reduce congestion and have advanced fuel economy by lowering emissions [1]. Autonomous cars assist people in their daily lives by providing reliable and safe transportation services to elderly and disabled people, handling parking problems, and eliminating a substantial number of accidents previously caused by human errors [2].

Gartner predicts that 250 million cars will be connected with each other by the end of 2020. Another report reveals that the artificial intelligence (AI) market is expected to be valued at $11,000 million by 2025 (accessed on: 25 Oct. 2018 https://medium.com/datadriveninvestor/ artificial-intelligence-and-autonomous-vehicles-ae877feb6cd2). IHS Markit anticipates that the installation rate of AI-based systems will grow up to 109 percent in 2025. McKinsey estimates that autonomous cars will produce a substantial revenue reaching $450 billion to $750 billion by 2030 (accessed on: 28 October 2018 https://itpeernetwork.intel.com/5g-key-fully-realizing-connected-autonomous-vehicles/). However, the development of autonomous cars requires contemporary solutions in terms of perception, planning, and control. Although autonomous cars are usually equipped with powerful computing and sensing technologies based on heterogeneous architectures, many inherent challenges associated with communication and networking technologies, privacy and security, real-time data analytics, data transmission, and limited bandwidth hinder autonomous cars from becoming a mainstream technology.

Figure 1 illustrates the concept of autonomous driving cars in smart cities. Furthermore, Fig. 2 depicts the core requirements that need to be fulfilled for enabling autonomous driving cars.

Fig. 1: Overview of autonomous driving cars in a smart city [3]

Fig. 2: Autonomous driving cars for smart cities requirements [3]

▶ Research Issues

 
  • Security
  • Radar interference management
  • Heterogeneous vehicular networks
  • Artificial intelligence for autonomous cars
  • Edge-assisted autonomous driving cars


  • ▶ References

     
    1. R. Hussain and S. Zeadally, “Autonomous Cars: Research Results, Issues and Future Challenges,” IEEE Commun. Surveys & Tutorials, vol. 21, no. 2, 2018, pp. 1275-1313.
    2. S. Kuutti et al., “A Survey of the State-of-the-Art Localization Techniques and Their Potentials for Autonomous Vehicle Applications,” IEEE Internet of Things J., vol. 5, no. 2, 2018, pp. 829?46
    3. Ibrar Yaqoob, Latif U. Khan, S. M. Ahsan Kazmi, Muhammad Imran, Nadra Guizani, Choong Seon Hong, "Autonomous Driving Cars in Smart Cities: Recent Advances, Requirements, and Challenges," IEEE Network Magazine, Vol.34, Issue 1, pp.174-181, Jan. 2020


    ▶ Achievements

     
    1. Ibrar Yaqoob, Latif U. Khan, S. M. Ahsan Kazmi, Muhammad Imran, Nadra Guizani, Choong Seon Hong, "Autonomous Driving Cars in Smart Cities: Recent Advances, Requirements, and Challenges," IEEE Network Magazine, Vol.34, Issue 1, pp.174-181, Jan. 2020
    2. Choong Seon Hong, Ndikumana Anselme, "DEEP LEARNING BASED CACHING SYSTEM AND METHOD FOR SELF-DRIVING CAR IN MULTI-ACCESS EDGE COMPUTING", 등록번호 : US10623782, 2020.04.14 (특허권자:경희대학교 산학협력단)
    3. Anselme Ndikumana, Nguyen H. Tran, Do Hyeon Kim, Ki Tae Kim, and Choong Seon Hong, "Deep Learning Based Caching for Self-Driving Cars in Multi-access Edge Computing," IEEE Transactions on Intelligent Transportation Systems, DOI:10.1109/TITS.2020.2976572.
    4. S.M. Ahsan Kazmi, Tri Nguyen Dang, Ibrar Yaqoob, Anselme Ndikumana, Ejaz Ahmed, Rasheed Hussain, and Choong Seon Hong,"Infotainment Enabled Smart Cars: A Joint Communication, Caching and Computation Approach," IEEE Transactions on Vehicular Technology, Vol.68, No. 9, pp.8408-8420, Sept. 2019
    5. Anselme Ndikumana, Choong Seon Hong, "Federated Learning Approach for Passenger-Centric Infotainment Services in Autonomous Cars", 2019년 한국컴퓨터종합학술대회(KCC 2019), 2019.6.26~6.28.
    6. Anselme Ndikumana, Choong Seon Hong, "Deep Learning Approach for In-Car Caching in Multi-Access Edge Computing", 2018년 한국컴퓨터종합학술대회(KCC 2018), 2018.6.20~6.22
    7. 홍충선, NDIKUMANA ANSELME, "Deep Learning Based Caching for Self-Driving Car in Multi-access Edge Computing", 출원번호: 18206202.6, 2018.11.14 (출원인: 경희대학교 산학협력단)
    8. VanDung Nguyen, Oanh Tran Thi Kim, Tri Nguyen Dang, Seung Il Moon and Choong Seon Hong, "An Efficient and Reliable Green Light Optimal Speed Advisory System for Autonomous Cars" The 18th Asia-Pacific Network Operations and Management Symposium(APNOMS 2016), Oct. 5-7, 2016, Kanazawa, Japan