Review of Autonomous Intelligent Vehicles for Urban Driving and Parking
Abstract
:1. Introduction
2. Advanced Driver-Assistance System
3. The Concepts of Autonomous Driving
3.1. Moving Out
3.2. On the Road
3.3. Parking
4. Research Gaps in Autonomous Driving
5. Possible Social Issues Caused by Autonomous Driving
6. Discussion
7. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Alam, F.; Mehmood, R.; Katib, I.; Albogami, N.N.; Albeshri, A. Data fusion and IoT for smart ubiquitous environments: A survey. IEEE Access 2017, 5, 9533–9554. [Google Scholar] [CrossRef]
- Munoz, R.; Vilalta, R.; Yoshikane, N.; Casellas, R.; Martinez, R.; Tsuritani, T.; Morita, I. Integration of IoT, Transport SDN, and edge/cloud computing for dynamic distribution of IoT analytics and efficient use of network resources. J. Lightwave Technol. 2018, 36, 1420–1428. [Google Scholar] [CrossRef]
- Frustaci, M.; Pace, P.; Aloi, G.; Fortino, G. Evaluating critical security issues of the IoT World: Present and future challenges. IEEE Internet Things J. 2018, 5, 2483–2495. [Google Scholar] [CrossRef]
- Ngu, A.H.; Gutierrez, M.; Metsis, V.; Nepal, S.; Quan, Z.S. IoT middleware: A survey on issues and enabling technologies. IEEE Internet Things J. 2017, 4, 1–20. [Google Scholar] [CrossRef]
- Kannan, M.; Mary, L.W.; Priya, C.; Manikandan, R. Towards smart city through virtualized and computerized car parking system using arduino in the internet of things. In Proceedings of the 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA), Gunupur, India, 13–14 March 2020; pp. 1–6. [Google Scholar]
- Kuutti, S.; Fallah, S.; Katsaros, K.; Dianati, M.; Mccullough, F.; Mouzakitis, A. A survey of the state-of-the-art localization techniques and their potentials for autonomous vehicle applications. IEEE Internet Things J. 2018, 5, 829–846. [Google Scholar] [CrossRef]
- Kong, L.; Khan, M.K.; Wu, F.; Chen, G.; Zeng, P. Millimeter-wave wireless communications for IoT-cloud supported autonomous vehicles: Overview, design, and challenges. IEEE Commun. Mag. 2017, 55, 62–68. [Google Scholar] [CrossRef]
- Honnaiah, P.J.; Maturo, N.; Chatzinotas, S. Foreseeing semi-persistent scheduling in mode-4 for 5G enhanced V2X communication. In Proceedings of the 2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, 10–13 January 2020; pp. 1–2. [Google Scholar]
- Li, L.; Liu, Y.; Wang, J.; Deng, W.; Oh, H. Human dynamics based driver model for autonomous car. IET Intell. Transp. Syst. 2016, 10, 545–554. [Google Scholar] [CrossRef]
- Andresen, L.; Brandemuehl, A.; Honger, A.; Kuan, B.; Vodisch, N.; Blum, H.; Reijgwart, V.; Bernreiter, L.; Schaupp, L.; Chung, J.J.; et al. Accurate mapping and planning for autonomous racing. In Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, 24 October–24 January 2020; pp. 4743–4749. [Google Scholar]
- Bensekrane, I.; Kumar, P.; Melingui, A.; Coelen, V.; Amara, Y.; Chettibi, T.; Merzouki, R. Energy Planning for Autonomous Driving of an Over-Actuated Road Vehicle. IEEE Trans. Intell. Transp. Syst. 2021, 22, 1114–1124. [Google Scholar] [CrossRef]
- Choi, Y.-J.; Hur, J.; Jeong, H.-Y.; Joo, C. Special issue on V2X communications and networks. J. Commun. Netw. 2017, 19, 205–208. [Google Scholar] [CrossRef]
- Chen, S.; Hu, J.; Shi, Y.; Peng, Y.; Fang, J.; Zhao, R.; Zhao, L. Vehicle-to-Everything (v2x) Services Supported by LTE-based Systems and 5g. IEEE Commun. Stand. Mag. 2017, 1, 70–76. [Google Scholar] [CrossRef]
- Bai, B.; Chen, W.; Letaief, K.B.; Cao, Z. Low complexity outage optimal distributed channel allocation for vehicle-to-vehicle communications. IEEE J. Sel. Areas Commun. 2011, 29, 161–172. [Google Scholar] [CrossRef]
- Zhang, R.; Cheng, X.; Yao, Q.; Wang, C.-X.; Yang, Y.; Jiao, B. Interference graph-based resource-sharing schemes for vehicular networks. IEEE Trans. Veh. Technol. 2013, 62, 4028–4039. [Google Scholar] [CrossRef] [Green Version]
- Du, L.; Dao, H. Information dissemination delay in vehicle-to-vehicle communication networks in a traffic stream. IEEE Trans. Intell. Transp. Syst. 2015, 16, 66–80. [Google Scholar] [CrossRef]
- Mei, J.; Zheng, K.; Zhao, L.; Teng, Y.; Wang, X. A latency and reliability guaranteed resource allocation scheme for LTE V2V communication systems. IEEE Trans. Wirel. Commun. 2018, 17, 3850–3860. [Google Scholar] [CrossRef]
- Belanovic, P.; Valerio, D.; Paier, A.; Zemen, T.; Ricciato, F.; Mecklenbrauker, C.F. On wireless links for vehicle-to-infrastructure communications. IEEE Trans. Veh. Technol. 2010, 59, 269–282. [Google Scholar] [CrossRef]
- Liu, N.; Liu, M.; Cao, J.; Chen, G.; Lou, W. When transportation meets communication: V2P over VANETs. In Proceedings of the 2010 IEEE 30th International Conference on Distributed Computing Systems, Genova, Italy, 21–25 June 2010; pp. 567–576. [Google Scholar]
- Lee, S.; Kim, D. An energy efficient vehicle to pedestrian communication method for safety applications. Wirel. Pers. Commun. 2016, 86, 1845–1856. [Google Scholar] [CrossRef]
- Merdrignac, P.; Shagdar, O.; Nashashibi, F. Fusion of perception and V2P communication systems for the safety of vulnerable road users. IEEE Trans. Intell. Transp. Syst. 2017, 18, 1740–1751. [Google Scholar] [CrossRef] [Green Version]
- Campolo, C.; Molinaro, A.; Iera, A.; Menichella, F. 5G network slicing for vehicle-to-everything services. IEEE Wirel. Commun. 2017, 24, 38–45. [Google Scholar] [CrossRef]
- Abboud, K.; Omar, H.A.; Zhuang, W. Interworking of DSRC and cellular network technologies for V2X Communications: A Survey. IEEE Trans. Veh. Technol. 2016, 65, 9457–9470. [Google Scholar] [CrossRef]
- Wei, Q.; Wang, L.; Feng, Z.; Ding, Z. Wireless Resource Management in LTE-U Driven Heterogeneous V2X Communication Networks. IEEE Trans. Veh. Technol. 2018, 67, 7508–7522. [Google Scholar] [CrossRef]
- Naik, G.; Choudhury, B.; Park, J. IEEE 802.11bd & 5G NR V2X: Evolution of Radio Access Technologies for V2X Communications. IEEE Access 2019, 7, 70169–70184. [Google Scholar]
- Budisusila, E.N.; Arifin, B.; Prasetyowati, S.A.D.; Suprapto, B.Y.; Nawawi, Z. Artificial Neural Network Algorithm for Autonomous Vehicle Ultrasonic Multi-Sensor System. In Proceedings of the 2020 10th Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS), Malang, Indonesia, 26–28 August 2020; pp. 128–131. [Google Scholar]
- Dueholm, J.V.; Kristoffersen, M.S.; Satzoda, R.K.; Moeslund, T.B.; Trivedi, M.M. Trajectories and Maneuvers of Surrounding Vehicles With Panoramic Camera Arrays. IEEE Trans. Intell. Veh. 2016, 1, 203–214. [Google Scholar] [CrossRef]
- Han, L.; Zheng, K.; Zhao, L.; Wang, X.; Shen, X. Short-term traffic prediction based on DeepCluster in large-scale road networks. IEEE Trans. Veh. Technol. 2019, 68, 12301–12313. [Google Scholar] [CrossRef]
- Siegel, J.E.; Erb, D.C.; Sarma, S.E. A survey of the connected vehicle landscape—Architectures, enabling technologies, applications, and development areas. IEEE Trans. Intell. Transp. Syst. 2018, 19, 2391–2406. [Google Scholar] [CrossRef] [Green Version]
- Shabir, B.; Khan, M.A.; Rahman, A.U.; Malik, A.W.; Wahid, A. Congestion avoidance in vehicular networks: A contemporary survey. IEEE Access 2019, 7, 173196–173215. [Google Scholar] [CrossRef]
- Machardy, Z.; Khan, A.; Obana, K.; Iwashina, S. V2X access technologies: Regulation, research, and remaining challenges. IEEE Commun. Surv. Tutor. 2018, 20, 1858–1877. [Google Scholar] [CrossRef]
- Hu, Q.; Luo, F. Review of secure communication approaches for in-vehicle network. Int. J. Automot. Technol. 2018, 19, 879–894. [Google Scholar] [CrossRef]
- Masini, B.M.; Bazzi, A.; Zanella, A. A survey on the roadmap to mandate on board connectivity and enable V2V-based vehicular sensor networks. Sensors 2018, 18, 2207. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wang, X.; Mao, S.; Gong, M.X. An overview of 3GPP cellular vehicle-to-everything standards. GetMobile Mob. Comput. Commun. 2017, 21, 19–25. [Google Scholar] [CrossRef]
- Chen, L.; Englund, C. Cooperative intersection management: A survey. IEEE Trans. Intell. Transp. Syst. 2016, 17, 570–586. [Google Scholar] [CrossRef]
- Dixit, S.; Fallah, S.; Montanaro, U.; Dianati, M.; Stevens, A.; Mccullough, F.; Mouzakitis, A. Trajectory planning and tracking for autonomous overtaking: State-of-the-art and future prospects. Annu. Rev. Control 2018, 45, 76–86. [Google Scholar] [CrossRef]
- Bresson, G.; Alsayed, Z.; Yu, L.; Glaser, S. Simultaneous localization and mapping: A survey of current trends in autonomous driving. IEEE Trans. Intell. Veh. 2017, 2, 194–220. [Google Scholar] [CrossRef] [Green Version]
- Bousselham, M.; Benamar, N.; Addaim, A. A new security mechanism for vehicular cloud computing using fog computing system. In Proceedings of the 2019 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS), Fez, Morocco, 3–4 April 2019; pp. 1–4. [Google Scholar]
- Mekki, T.; Jabri, I.; Rachedi, A.; ben Jemaa, M. Vehicular cloud networks: Challenges, architectures, and future directions. Veh. Commun. 2017, 9, 268–280. [Google Scholar] [CrossRef]
- Boukerche, A.; De Grande, R.E. Vehicular cloud computing: Architectures, applications, and mobility. Comput. Netw. 2018, 135, 171–189. [Google Scholar] [CrossRef]
- Yang, Q.; Zhu, B.; Wu, S. An architecture of cloud-assisted information dissemination in vehicular networks. IEEE Access 2016, 4, 2764–2770. [Google Scholar] [CrossRef]
- Meneguette, R.I.; Boukerche, A.; de Grande, R. SMART: An efficient resource search and management scheme for vehicular cloud-connected system. In Proceedings of the 2016 IEEE Global Communications Conference (GLOBECOM), Washington, DC, USA, 4–8 December 2016; pp. 1–6. [Google Scholar]
- De Souza, A.B.; Rego, P.A.L.; de Souza, J.N. Exploring computation offloading in vehicular clouds. In Proceedings of the 2019 IEEE 8th International Conference on Cloud Networking (CloudNet), Coimbra, Portugal, 4–6 November 2019; pp. 1–4. [Google Scholar]
- Sharma, V.; You, I.; Yim, K.; Chen, I.-R.; Cho, J.-H. BRIoT: Behavior rule specification-based misbehavior detection for IoT-embedded cyber-physical systems. IEEE Access 2019, 7, 118556–118580. [Google Scholar] [CrossRef]
- Salahuddin, M.A.; Al-Fuqaha, A.; Guizani, M. Software-defined networking for rsu clouds in support of the internet of vehicles. IEEE Internet Things J. 2015, 2, 133–144. [Google Scholar] [CrossRef]
- Ramwala, O.A.; Paunwala, C.N.; Paunwala, M.C. Image de-raining for driver assistance systems using U-Net based GAN. In Proceedings of the 2019 IEEE International Conference on Signal Processing, Information, Communication & Systems (SPICSCON), Dhaka, Bangladesh, 28–30 November 2019; pp. 23–26. [Google Scholar]
- Eichelberger, A.H.; McCartt, A.T. Toyota drivers’ experiences with dynamic radar cruise control, pre-collision system, and lane-keeping assist. J. Saf. Res. 2016, 56, 67–73. [Google Scholar] [CrossRef]
- Hubele, N.; Kennedy, K. Forward collision warning system impact. Traffic Inj. Prev. 2018, 19, S78–S83. [Google Scholar] [CrossRef]
- Patra, S.; Veelaert, P.; Calafate, C.T.; Cano, J.-C.; Zamora, W.; Manzoni, P.; González, F. A forward collision warning system for smartphones using image processing and V2V communication. Sensors 2018, 18, 2672. [Google Scholar] [CrossRef] [Green Version]
- Motamedidehkordi, N.; Amini, S.; Hoffmann, S.; Busch, F.; Fitriyanti, M.R. Modeling tactical lane-change behavior for automated vehicles: A supervised machine learning approach. In Proceedings of the 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), Naples, Italy, 26–28 June 2017; pp. 268–273. [Google Scholar]
- Yan, Z.; Yang, K.; Wang, Z.; Yang, B.; Kaizuka, T.; Nakano, K. Intention-based lane changing and lane keeping haptic guidance steering system. IEEE Trans. Intell. Veh. 2021, in press. [Google Scholar]
- Katzourakis, D.I.; Lazic, N.; Olsson, C.; Lidberg, M.R. Driver steering override for lane-keeping aid using computer-aided engineering. IEEE/ASME Trans. Mechatron. 2015, 20, 1543–1552. [Google Scholar] [CrossRef]
- Shen, D.; Yi, Q.; Li, L.; Tian, R.; Chien, S.; Chen, Y.; Sherony, R. Test scenarios development and data collection methods for the evaluation of vehicle road departure prevention systems. IEEE Trans. Intell. Veh. 2019, 4, 337–352. [Google Scholar] [CrossRef]
- Sternlund, S.; Strandroth, J.; Rizzi, M.; Lie, A.; Tingvall, C. The effectiveness of lane departure warning systems—A reduction in real-world passenger car injury crashes. Traffic Inj. Prev. 2017, 18, 225–229. [Google Scholar] [CrossRef] [PubMed]
- Abdullahi, A.; Akkaya, S. Adaptive cruise control: A model reference adaptive control approach. In Proceedings of the 2020 24th International Conference on System Theory, Control and Computing (ICSTCC), Sinaia, Romania, 8–10 October 2020; pp. 904–908. [Google Scholar]
- Li, Y.; Li, Z.; Wang, H.; Wang, W.; Xing, L. Evaluating the safety impact of adaptive cruise control in traffic oscillations on freeways. Accid. Anal. Prev. 2017, 104, 137–145. [Google Scholar] [CrossRef] [PubMed]
- Plessen, M.G.; Bernardini, D.; Esen, H.; Bemporad, A. Spatial-based predictive control and geometric corridor planning for adaptive cruise control coupled with obstacle avoidance. IEEE Trans. Control. Syst. Technol. 2018, 26, 38–50. [Google Scholar] [CrossRef]
- Hu, J.; Xu, L.; He, X.; Meng, W. Abnormal driving detection based on normalized driving behavior. IEEE Trans. Veh. Technol. 2017, 66, 6645–6652. [Google Scholar] [CrossRef]
- Adochiei, I.-R.; Știrbu, O.-I.; Adochiei, N.-I.; Pericle-Gabriel, M.; Larco, C.M.; Mustat, S.M. Diana costin drivers’ drowsiness detection and warning systems for critical infrastructures. In Proceedings of the 2020 International Conference on e-Health and Bioengineering (EHB), Iasi, Romania, 29–30 October 2020; pp. 1–4. [Google Scholar]
- Saito, Y.; Itoh, M.; Inagaki, T. driver assistance system with a dual control scheme: Effectiveness of identifying driver drowsiness and preventing lane departure accidents. IEEE Trans. Hum. Mach. Syst. 2016, 46, 660–671. [Google Scholar] [CrossRef] [Green Version]
- Yin, J.L.; Chen, B.H.; Lai, K.H.B.; Li, Y. Automatic dangerous driving intensity analysis for advanced driver assistance systems from multimodal driving signals. IEEE Sens. J. 2018, 18, 4785–4794. [Google Scholar] [CrossRef]
- Shahzad, A.; Choi, J.-Y.; Xiong, N.; Kim, Y.-G.; Lee, M. Centralized connectivity for multiwireless edge computing and cellular platform: A smart vehicle parking system. Wirel. Commun. Mob. Comput. 2018, 2018, 7243875. [Google Scholar] [CrossRef] [Green Version]
- Tsai, M.-F.; Kiong, Y.C.; Sinn, A. Smart service relying on Internet of Things technology in parking systems. J. Supercomput. 2018, 74, 4315–4338. [Google Scholar] [CrossRef]
- Sadreddini, Z.; Guner, S.; Erdinc, O. Design of a decision-based multi-criteria reservation system for the EV parking lot. IEEE Trans. Transp. Electrif. 2021, in press. [Google Scholar] [CrossRef]
- Ampuni, A.; Fonataba, S.; Fitrianto, A.; Wang, G. smart parking system with automatic cashier machine utilize the iot technology. In Proceedings of the 2019 International Conference on ICT for Smart Society (ICISS), Bandung, Indonesia, 19–20 November 2019; pp. 1–4. [Google Scholar]
- Hanif, N.H.H.M.; Badiozaman, M.H.; Daud, H. Smart parking reservation system using short message services (SMS). In Proceedings of the 2010 International Conference on Intelligent and Advanced Systems, Manila, Philippines, 15–17 June 2010; pp. 1–5. [Google Scholar]
- Sheelarani, P.; Anand, S.P.; Shamili, S.; Sruthi, K. Effective car parking reservation system based on internet of things technologies. In Proceedings of the 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave), Coimbatore, India, 29 February–1 March 2016; pp. 1–4. [Google Scholar]
- Shih, S.-E.; Tsai, W.-H. A Convenient Vision-Based System for Automatic Detection of Parking Spaces in Indoor Parking Lots Using Wide-Angle Cameras. IEEE Trans. Veh. Technol. 2014, 63, 2521–2532. [Google Scholar] [CrossRef] [Green Version]
- Baroffio, L.; Bondi, L.; Cesana, M.; Redondi, A.E.; Tagliasacchi, M. A visual sensor network for parking lot occupancy detection in Smart Cities. In Proceedings of the IEEE World Forum Internet Things, WF-IoT 2015, Milan, Italy, 14–16 December 2015; pp. 745–750. [Google Scholar]
- Valipour, S.; Siam, M.; Stroulia, E.; Jagersand, M. Parking-stall vacancy indicator system, based on deep convolutional neural networks. In Proceedings of the IEEE World Forum Internet Things, WF-IoT 2016, Reston, VA, USA, 12–14 December 2016; pp. 655–660. [Google Scholar]
- Amato, G.; Carrara, F.; Falchi, F.; Gennaro, C.; Meghini, C.; Vairo, C. Deep learning for decentralized parking lot occupancy detection. Expert Syst. Appl. 2017, 72, 327–334. [Google Scholar] [CrossRef]
- Cho, W.; Park, S.; Kim, M.J.; Han, S.; Kim, M.; Kim, T.; Kim, J.; Paik, J. Robust parking occupancy monitoring system using random forests. In Proceedings of the 2018 International Conference on Electronics, Information, and Communication (ICEIC), Honolulu, HI, USA, 24–27 January 2018; pp. 1–4. [Google Scholar]
- Rajalekshmi, R.; Radhakrishnan, B.; Suresh, L.P. Intelligent parking space detection and number plate extraction. In Proceedings of the 2017 International Conference on Circuit, Power and Computing Technologies (ICCPCT), Kollam, India, 20–21 April 2017. [Google Scholar]
- Paidi, V.; Fleyeh, H.; Håkansson, J.; Nyberg, R.G. Smart parking sensors, technologies and applications for open parking lots: A review. IET Intell. Transp. Syst. 2018, 12, 735–741. [Google Scholar] [CrossRef]
- Yugopuspito, P.; Herwansyah, R.A.; Krisnadi, D.; Cahya, S.; Panduwinata, F. Performance notification in a reservation-based parking system. In Proceedings of the 2016 International Seminar on Intelligent Technology and Its Applications (ISITIA), Lombok, Indonesia, 28–30 July 2016; pp. 429–434. [Google Scholar]
- Farooqi, N.; Alshehri, S.; Nollily, S.; Najmi, L.; Alqurashi, G.; Alrashedi, A. UParking: Developing a smart parking management system using the internet of things. In Proceedings of the 2019 Sixth HCT Information Technology Trends (ITT), Ras Al Khaimah, United Arab Emirates, 20–21 November 2019; pp. 214–218. [Google Scholar]
- Kotb, A.O.; Shen, Y.-C.; Huang, Y. Smart parking guidance, monitoring and reservations: A review. IEEE Intell. Transp. Syst. Mag. 2017, 9, 6–16. [Google Scholar] [CrossRef]
- Xu, X.; Grizzle, J.W.; Tabuada, P.; Ames, A.D. Correctness guarantees for the composition of lane keeping and adaptive cruise control. IEEE Trans. Autom. Sci. Eng. 2018, 15, 1216–1229. [Google Scholar] [CrossRef] [Green Version]
- Fahmy, H.M.; El Ghany, M.A.A.; Baumann, G. Vehicle risk assessment and control for lane-keeping and collision avoidance at low-speed and high-speed scenarios. IEEE Trans. Veh. Technol. 2018, 67, 4806–4818. [Google Scholar] [CrossRef]
- Kang, C.M.; Lee, S.-H.; Chung, C.C. Multirate lane-keeping system with kinematic vehicle model. IEEE Trans. Veh. Technol. 2018, 67, 9211–9222. [Google Scholar] [CrossRef]
- Rinaldi, M.; Picarelli, E.; Laskaris, G.; d’Ariano, A.; Viti, F. Mixed hybrid and electric bus dynamic fleet management in urban networks: A model predictive control approach. In Proceedings of the 2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), Cracow, Poland, 5–7 June 2019; pp. 1–8. [Google Scholar]
- Martinez-Garcia, M.; Zhang, Y.; Gordon, T. Modeling Lane Keeping by a Hybrid Open-Closed-Loop Pulse Control Scheme. IEEE Trans. Ind. Inform. 2016, 12, 2256–2265. [Google Scholar] [CrossRef] [Green Version]
- Kim, W.; Son, Y.S.; Chung, C.C. Torque-overlay-based robust steering wheel angle control of electrical power steering for a lane-keeping system of automated vehicles. IEEE Trans. Veh. Technol. 2016, 65, 4379–4392. [Google Scholar] [CrossRef]
- Wu, S.J.; Chiang, H.H.; Perng, J.W.; Chen, C.J.; Wu, B.F.; Lee, T.T. The heterogeneous systems integration design and implementation for lane keeping on a vehicle. IEEE Trans. Intell. Transp. Syst. 2008, 9, 246–263. [Google Scholar] [CrossRef]
- Wang, W.; Zhao, D. Evaluation of Lane Departure Correction Systems Using a Regenerative Stochastic Driver Model. IEEE Trans. Intell. Veh. 2017, 2, 221–232. [Google Scholar] [CrossRef]
- Nobukawa, K.; Bao, S.; Leblanc, D.J.; Zhao, D.; Peng, H.; Pan, C.S. Gap Acceptance During Lane Changes by Large-Truck Drivers—An Image-Based Analysis. IEEE Trans. Intell. Transp. Syst. 2016, 17, 772–781. [Google Scholar] [CrossRef]
- Zhao, D.; Lam, H.; Peng, H.; Bao, S.; LeBlanc, D.J.; Nobukawa, K.; Pan, C.S. Accelerated evaluation of automated vehicles safety in lane-change scenarios based on importance sampling techniques. IEEE Trans. Intell. Transp. Syst. 2017, 18, 595–607. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Dang, R.; Wang, J.; Li, S.E.; Li, K. Coordinated adaptive cruise control system with lane-change assistance. IEEE Trans. Intell. Transp. Syst. 2015, 16, 2373–2383. [Google Scholar] [CrossRef]
- Bevly, D.; Cao, X.; Gordon, M.; Ozbilgin, G.; Kari, D.; Nelson, B.; Woodruff, J.; Barth, M.; Murray, C.; Kurt, A.; et al. Lane change and merge maneuvers for connected and automated vehicles: A survey. IEEE Trans. Intell. Veh. 2016, 1, 105–120. [Google Scholar] [CrossRef]
- Desiraju, D.; Chantem, T.; Heaslip, K. Minimizing the disruption of traffic flow of automated vehicles during lane changes. IEEE Trans. Intell. Transp. Syst. 2015, 16, 1249–1258. [Google Scholar] [CrossRef] [Green Version]
- Suh, J.; Chae, H.; Yi, K. Stochastic model-predictive control for lane change decision of automated driving vehicles. IEEE Trans. Veh. Technol. 2018, 67, 4771–4782. [Google Scholar] [CrossRef]
- Ji, J.; Khajepour, A.; Melek, W.W.; Huang, Y. Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints. IEEE Trans. Veh. Technol. 2017, 66, 952–964. [Google Scholar] [CrossRef]
- Petrov, P.; Nashashibi, F. Modeling and nonlinear adaptive control for autonomous vehicle overtaking. IEEE Trans. Intell. Transp. Syst. 2014, 15, 1643–1656. [Google Scholar] [CrossRef] [Green Version]
- Chae, H.; Yi, K. Virtual target-based overtaking decision, motion planning, and control of autonomous vehicles. IEEE Access 2020, 8, 51363–51376. [Google Scholar] [CrossRef]
- Athree, M.; Jayasiri, A. Vision-based automatic warning system to prevent dangerous and illegal vehicle overtaking. In Proceedings of the 2020 International Research Conference on Smart Computing and Systems Engineering (SCSE), Colombo, Sri Lanka, 24–24 September 2020; pp. 25–30. [Google Scholar]
- Chiang, H.-H.; Chen, Y.-L.; Wu, B.-F.; Lee, T.-T. Embedded driver-assistance system using multiple sensors for safe overtaking maneuver. IEEE Syst. J. 2014, 8, 681–698. [Google Scholar] [CrossRef]
- Hu, H.; Smith, S.F.; Goldstein, R. Cooperative schedule-driven intersection control with connected and autonomous vehicles. In Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, 3–8 November 2019; pp. 1668–1673. [Google Scholar]
- Wuthishuwong, C.; Traechtler, A. Consensus-based local information coordination for the networked control of the autonomous intersection management. Complex Intell. Syst. 2017, 3, 17–32. [Google Scholar] [CrossRef] [Green Version]
- Liu, C.; Lin, C.-W.; Shiraishi, S.; Tomizuka, M. Distributed conflict resolution for connected autonomous vehicles. IEEE Trans. Intell. Veh. 2018, 3, 18–29. [Google Scholar] [CrossRef]
- Qian, X.; Altché, F.; Grégoire, J.; Fortelle, A. Autonomous intersection management systems: Criteria, implementation and evaluation. IET Intell. Transp. Syst. 2017, 11, 182–189. [Google Scholar] [CrossRef]
- Butakov, V.A.; Ioannou, P. Personalized driver assistance for signalized intersections using V2I Communication. IEEE Trans. Intell. Transp. Syst. 2016, 17, 1910–1919. [Google Scholar] [CrossRef]
- Fayazi, S.A.; Vahidi, A. Mixed-integer linear programming for optimal scheduling of autonomous vehicle intersection crossing. IEEE Trans. Intell. Veh. 2018, 3, 287–299. [Google Scholar] [CrossRef]
- Medina, A.I.M.; Van De Wouw, N.N.; Nijmeijer, H.H. Cooperative intersection control based on virtual platooning. IEEE Trans. Intell. Transp. Syst. 2018, 19, 1727–1739. [Google Scholar] [CrossRef] [Green Version]
- Barnes, D.; Gadd, M.; Murcutt, P.; Newman, P.; Posner, I. The oxford radar robotcar dataset: A radar extension to the oxford robotcar dataset. In Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 31 May–31 August 2020; pp. 6433–6438. [Google Scholar]
- Jindaprakai, N.; Nuchitprasitchai, S. Intelligent parking system using multiple sensor detection. In Proceedings of the 2019 Research, Invention, and Innovation Congress (RI2C), Bangkok, Thailand, 11–13 December 2019; pp. 1–4. [Google Scholar]
- Williams, D.; de Martini, D.; Gadd, M.; Marchegiani, L.; Newman, P. Keep off the grass: Permissible driving routes from radar with weak audio supervision. In Proceedings of the 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece, 20–23 September 2020; pp. 1–6. [Google Scholar]
- Dubois, J.M.; Vincent, F.; Bonacci, D. Sonar and radar SAR processing for parking lot detection. In Proceedings of the 2011 12th International Radar Symposium (IRS), Leipzig, Germany, 7–9 September 2011; pp. 1–6. [Google Scholar]
- Barsan, I.A.; Liu, P.; Pollefeys, M.; Geiger, A. Robust Dense Mapping for Large-Scale Dynamic Environments. In Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia, 21–25 May 2018; pp. 7510–7517. [Google Scholar]
- Chen, J.; Liu, Y.; Carey, S.J.; Dudek, P. Proximity estimation using vision features computed on sensor. In Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 31 May–31 August 2020; pp. 2689–2695. [Google Scholar]
- Gamal, O.; Imran, M.; Roth, H.; Wahrburg, J. Assistive parking systems knowledge transfer to end-to-end deep learning for autonomous parking. In Proceedings of the 2020 6th International Conference on Mechatronics and Robotics Engineering (ICMRE), Barcelona, Spain, 12–15 February 2020; pp. 216–221. [Google Scholar]
- Zhang, X.; Ma, Z.; He, Z.; Wang, Z. Vision-based UAV obstacle avoidance algorithm on the embedded platform. In Proceedings of the 2019 IEEE International Conference on Unmanned Systems and Artificial Intelligence (ICUSAI), Xi’an, China, 22–24 November 2019; pp. 85–90. [Google Scholar]
- Jhang, J.-H.; Lian, F.-L. An autonomous parking system of optimally integrating bidirectional rapidly-exploring random trees* and parking-oriented model predictive control. IEEE Access 2020, 8, 163502–163523. [Google Scholar] [CrossRef]
- Khamgerd, S.; Khoenkaw, P. Pull-based algorithm for parking space sensor data reading. In Proceedings of the 2019 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT-NCON), Nan, Thailand, 30 January–2 February 2019; pp. 339–342. [Google Scholar]
- Li, B.; Wang, K.; Shao, Z. Time-optimal maneuver planning in automatic parallel parking using a simultaneous dynamic optimization approach. IEEE Trans. Intell. Transp. Syst. 2016, 17, 3263–3274. [Google Scholar] [CrossRef]
- Liu, W.; Li, Z.; Li, L.; Wang, F.-Y. Parking Like a Human: A Direct Trajectory Planning Solution. IEEE Trans. Intell. Transp. Syst. 2017, 18, 3388–3397. [Google Scholar] [CrossRef]
- Oetiker, M.; Baker, G.; Guzzella, L. A Navigation-field-based semi-autonomous nonholonomic vehicle-parking assistant. IEEE Trans. Veh. Technol. 2009, 58, 1106–1118. [Google Scholar] [CrossRef]
- Wijaya, K.T.; Bharoto, L.Y.; Purwanto, A.; Syamsuddin, E.Y. Vision-based parking assist system with bird- eye surround vision for reverse bay parking maneuver recommendation. In Proceedings of the 2020 International Electronics Symposium (IES), Surabaya, Indonesia, 29–30 September 2020; pp. 102–107. [Google Scholar]
- Chen, Y.; Peng, H.; Grizzle, J. Obstacle avoidance for low-speed autonomous vehicles with barrier function. IEEE Trans. Control Syst. Technol. 2018, 26, 194–206. [Google Scholar] [CrossRef]
- Funke, J.; Brown, M.; Erlien, S.M.; Gerdes, J.C. Collision avoidance and stabilization for autonomous vehicles in emergency scenarios. IEEE Trans. Control Syst. Technol. 2017, 25, 1204–1216. [Google Scholar] [CrossRef]
- Viriyasitavat, W.; Tonguz, O.K. Priority management of emergency vehicles at intersections using self-organized traffic control. In Proceedings of the 2012 IEEE Vehicular Technology Conference (VTC Fall), Québec City, QC, Canada, 3–6 September 2012; pp. 1–4. [Google Scholar]
- Masini, B.M.; Zanella, A.; Pasolini, G.; Bazzi, A.; Zabini, F.; Andrisano, O.; Mirabella, M.; Toppan, P. Toward the integration of ADAS capabilities in V2X communications for cooperative driving. In Proceedings of the 2020 AEIT International Conference of Electrical and Electronic Technologies for Automotive, Turin, Italy, 18–20 November 2020; pp. 1–6. [Google Scholar]
- Wooten, M.J.; Murrian, J.M.; LaChapelle, D.M.; Humphreys, T.; Narula, L.; Murrian, M.J.; Humphreys, E.T. ADAS Enhanced by 5G Connectivity: Volumes 1 and 2; National Technical Information Service: Springfield, VA, USA, 2018.
- Bazzi, A.; Masini, B.M.; Zanella, A.; de Castro, C.; Raffaelli, C.; Andrisano, O. Cellular aided vehicular named data networking. In Proceedings of the 2014 International Conference on Connected Vehicles and Expo (ICCVE), Vienna, Austria, 3–7 November 2014; pp. 747–752. [Google Scholar]
- Masini, B.M.; Silva, C.M.; Balador, A. The use of meta-surfaces in vehicular networks. J. Sens. Actuator Netw. 2020, 9, 15. [Google Scholar] [CrossRef] [Green Version]
- Guanetti, J.; Kim, Y.; Borrelli, F. Control of connected and automated vehicles: State of the art and future challenges. Annu. Rev. Control 2018, 45, 18–40. [Google Scholar] [CrossRef] [Green Version]
- Rios-Torres, J.; Malikopoulos, A.A. A survey on the coordination of connected and automated vehicles at intersections and merging at highway on-ramps. IEEE Trans. Intell. Transp. Syst. 2017, 18, 1066–1077. [Google Scholar] [CrossRef]
- Greenblatt, N.A. Self-driving cars and the law. IEEE Spectr. 2016, 53, 46–51. [Google Scholar] [CrossRef]
- Borenstein, J.; Herkert, J.; Miller, K. Self-driving cars: Ethical responsibilities of design engineers. IEEE Technol. Soc. Mag. 2017, 36, 67–75. [Google Scholar] [CrossRef]
- Birnbacher, D.; Birnbacher, W. Fully autonomous driving: Where technology and ethics meet. IEEE Intell. Syst. 2017, 32, 3–4. [Google Scholar] [CrossRef] [Green Version]
- Coca-Vila, I. Self-driving cars in dilemmatic situations: An approach based on the theory of justification in criminal law. Crim. Law Philos. 2018, 12, 59–82. [Google Scholar] [CrossRef]
- Dhar, V. Equity, Safety, and privacy in the autonomous vehicle era. Computer 2016, 49, 80–83. [Google Scholar] [CrossRef]
- Urooj, S.; Feroz, I.; Ahmad, N. Systematic literature review on user interfaces of autonomous cars: Liabilities and responsibilities. In Proceedings of the 2018 International Conference on Advancements in Computational Sciences (ICACS), Lahore, Pakistan, 19–21 February 2018; pp. 1–10. [Google Scholar]
- Fournier, T. Will my next car be a libertarian or a utilitarian? Who will decide? IEEE Technol. Soc. Mag. 2016, 35, 40–45. [Google Scholar] [CrossRef]
- Lin, P. The Ethics of Autonomous Cars. The Atlantic. Available online: http://www.theatlantic.com/technology/archive/2013/10/theethics-of-autonomous-cars/280360/ (accessed on 24 April 2020).
- Ma, A. China Has Started Ranking Citizens with a Creepy ‘Social Credit’ System—Here’s What You Can Do Wrong, and the Embarrassing, Demeaning Ways They Can Punish You. Business Insider US. Available online: https://www.businessinsider.sg/china-social-credit-system-punishments-and-rewards-explained-2018-4/?r=US&IR=T (accessed on 20 April 2020).
- Chan, T.K.; Chin, C.S.; Chen, H.; Zhong, X. A comprehensive review of driver behavior analysis utilizing smartphones. IEEE Trans. Intell. Transp. Syst. 2020, 21, 4444–4475. [Google Scholar] [CrossRef]
- Solanke, T.U.; Ramachandaramurthy, V.K.; Yong, J.Y.; Pasupuleti, J.; Kasinathan, P.; Rajagopalan, A. A review of strategic charging–discharging control of grid-connected electric vehicles. J. Energy Storage 2020, 28, 101193. [Google Scholar] [CrossRef]
- Zou, Y.; Zhao, J.; Gao, X.; Chen, Y.; Tohidi, A. Experimental results of electric vehicles effects on low voltage grids. J. Clean. Prod. 2020, 255, 120270. [Google Scholar] [CrossRef]
- Arena, F.; Pau, G.; Severino, A. An overview on the current status and future perspectives of smart cars. Infrastructures 2020, 5, 53. [Google Scholar] [CrossRef]
- Das, H.; Rahman, M.; Li, S.; Tan, C. Electric vehicles standards, charging infrastructure, and impact on grid integration: A technological review. Renew. Sustain. Energy Rev. 2020, 120, 109618. [Google Scholar] [CrossRef]
- Pappalardo, G.; Cafiso, S.; di Graziano, A. A severino, decision tree method to analyze the performance of lane support systems. Sustainability 2021, 13, 846. [Google Scholar] [CrossRef]
- Ghahari, S.; Assi, L.; Carter, K.; Ghotbi, S. The Future of Hydrogen Fueling Systems for Fully Automated Vehicles; American Society of Civil Engineers (ASCE): Reston, VA, USA, 2019; pp. 66–76. [Google Scholar]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Chan, T.K.; Chin, C.S. Review of Autonomous Intelligent Vehicles for Urban Driving and Parking. Electronics 2021, 10, 1021. https://doi.org/10.3390/electronics10091021
Chan TK, Chin CS. Review of Autonomous Intelligent Vehicles for Urban Driving and Parking. Electronics. 2021; 10(9):1021. https://doi.org/10.3390/electronics10091021
Chicago/Turabian StyleChan, Teck Kai, and Cheng Siong Chin. 2021. "Review of Autonomous Intelligent Vehicles for Urban Driving and Parking" Electronics 10, no. 9: 1021. https://doi.org/10.3390/electronics10091021
APA StyleChan, T. K., & Chin, C. S. (2021). Review of Autonomous Intelligent Vehicles for Urban Driving and Parking. Electronics, 10(9), 1021. https://doi.org/10.3390/electronics10091021