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Chin, C.S. Autonomous Intelligent Vehicles for Urban. Encyclopedia. Available online: https://encyclopedia.pub/entry/9076 (accessed on 17 November 2024).
Chin CS. Autonomous Intelligent Vehicles for Urban. Encyclopedia. Available at: https://encyclopedia.pub/entry/9076. Accessed November 17, 2024.
Chin, Cheng Siong. "Autonomous Intelligent Vehicles for Urban" Encyclopedia, https://encyclopedia.pub/entry/9076 (accessed November 17, 2024).
Chin, C.S. (2021, April 27). Autonomous Intelligent Vehicles for Urban. In Encyclopedia. https://encyclopedia.pub/entry/9076
Chin, Cheng Siong. "Autonomous Intelligent Vehicles for Urban." Encyclopedia. Web. 27 April, 2021.
Autonomous Intelligent Vehicles for Urban
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This entry is adapted from the peer-reviewed paper 10.3390/electronics10091021

With the concept of Internet-of-Things, autonomous vehicles can provide higher driving efficiency, traffic safety, and freedom for the driver to perform other tasks. This paper first covers enabling technology involving a vehicle moving out of parking, traveling on the road, and parking at the destination. The development of autonomous vehicles relies on the data collected for deployment in actual road conditions. Research gaps and recommendations for autonomous intelligent vehicles are included

Internet-of-Things intelligent transport system fully autonomous vehicle enabling technology social dilemma

1. Introduction

The Internet of Things (IoT) enables billions of intelligent devices with processing, sensing, and actuating capabilities to connect to the internet and facilitate the sharing and collaboration of data [1][2][3]. Such platforms can be applied to smart homes, warning systems, smart cities, threat identification systems, the automotive field, and mobility [1][2][4]. In the automotive field, IoT allows the development of a vehicle capable of driving autonomously to provide higher driving efficiency, traffic safety, and freedom for the driver to perform other tasks [5][6][7][8]. The driving process can be described as a series of acceleration changes, direction changes, lane changes, and light changes [9]. To drive without human intervention, an autonomous vehicle should consider the overall situation [10], which requires five primary functions: localization, perception, planning, vehicle control, and system management [11].

The localization module is responsible for estimating vehicle position, and the perception module creates a model of the driving environment from multisensor information-fused data. Based on localization and perception information, the planning module then determines the vehicle’s maneuvers for safe navigation. The vehicle control module follows the planning module’s desired command by controlling the steering, acceleration, and braking. Finally, the system management module supervises the overall autonomous driving system [11]. However, the process becomes much more sophisticated when there are more elements to consider, such as other vehicles, pedestrians, or cyclists on the road. Therefore, to enable communication between an autonomous vehicle and other road elements, a vehicle communication system becomes an indispensable and critical component in an autonomous vehicle. Such communication system is commonly known as vehicle-to-everything (V2X) communication, which includes several scenarios such as vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-pedestrian (V2P), and vehicle-to-network (V2N) [12][13].

V2V consists of multiple vehicles in which any two vehicles can communicate with each other [14][15]. Thus, allowing the driver to be aware of other cars’ speed, acceleration or deceleration, and accidents such as collision and road departure can be avoided [16]. In contrast, V2I communication allows the vehicle to access the roadside infrastructure for vast area information dissemination [17]. Services include infotainment delivery, as well as safety-related information such as speed limits, safe distance warnings, lane-keeping support, intersection safety, traffic jam warnings, and accident warnings [18]. The idea of V2P is the exchange of information between a vehicle and a pedestrian through sensors and smart devices to avoid collision [19][20][21]. Finally, V2N puts vehicular user equipment in communication with a server that provides centralized control and traffic, road, and service information [22]. Therefore, the use of V2X communications coupled with existing vehicle-sensing capabilities provides the fundamentals for advanced applications targeted toward road safety, passenger infotainment, manufacturer services, and vehicle traffic optimization [23][24].

The development of such systems will ultimately rely on the data collected from actual interaction if they are to be effective when deployed in a real-life situation [25]. For example, machine vision uses image processing to monitor rear vehicles [26] and the trajectory analysis of surrounding vehicles [27]. Furthermore, historical data are used to determine the optimal control parameters for maximal fuel efficiency and saving [28]. Without full vehicle autonomy, data collected by in-car sensors can also be used to analyze driver behavior, which reduces the likelihood of impaired or drowsy driving [29].

There exists literature that has surveyed V2X communication and focused on the area of network connectivity and security [30][31][32][33][34]. Reviews that focus on other areas related to the autonomous vehicle have also been presented. Siegel et al. [29] summarized the state of the art of connected vehicles from the need for vehicle data, applications, enabling technologies, and challenges. Chen and Englund [35] reviewed cooperative intersection management where road users, infrastructure, and traffic control centers can communicate and coordinate traffic safely and efficiently. Their paper includes methods for both signalized and nonsignalized intersections with emphasis on nonsignalized intersections. Dixit et al. [36] provided a review in the area of autonomous overtaking. The authors showed that the two essential aspects of high-speed overtaking are vehicle dynamics and environmental constraints, as well as accurate knowledge of the environment and surrounding obstacles. Bresson et al. [37] provided a survey on localization techniques using onboard sensing systems and their combinations with V2V and V2I systems for autonomous vehicles and investigated their applicability. The authors of [38][39][40] published reviews on vehicular cloud computing, which is an extension of mobile cloud computing based on vehicular networks, and also reviewed several proposed cloud computing schemes [41][42][43][44][45]. Bousselham et al. [38] focused on applications, cloud formations, key management, intercloud communication systems, and aspects of privacy and security issues. Mekki et al. [39] focused on the challenges of vehicular cloud networks. Finally, Boukerche and De Grande [40] described solutions for vehicular clouds, featuring applications, services, and traffic models that can enable vehicular clouds in a more dynamic environment.

2. Advanced Driver-Assistance System

Prior to fully autonomous driving, the technology of advanced driver-assistance systems (ADASs) is briefly discussed. ADASs can help with monitoring, braking, and warning tasks to enhance safety conditions on the road. ADASs can perform parking assistance or monitoring. Connected technologies such as V2X and V2I, streetlights and traffic information, together with ADASs, can create a safer road for drivers and pedestrians. As ADASs continue to strive for more benefits, it is known that governments may soon require vehicles to install essential ADASs and their components over the next few years. It is necessary to highlight that the ADASs discussed here refer to technology to assist the driver during driving rather than an autonomously driven car. Current driver-assistance systems are gradually being equipped with more advanced technology. Most systems aim to provide parking assistance, forward collision warnings, lane-departure warnings, adaptive cruise control, and driver drowsiness detection [46].

Parking assistance systems aim to provide safe and comfortable backward parking. These systems work by reading data such as the magnitude of steering wheel rotation, speed, and lateral acceleration from the vehicle’s electronic stability control system to generate the backward parking trajectory. The rear camera then captures the vehicle’s rear view, and the trajectory is integrated into the view and displayed on the in-car monitor. Such a reference provides the driver with the information of where the vehicle is heading and prevents a crash while reversing.

Forward collision avoidance systems are designed to provide the driver with a visual and audible warning when they are too close to a vehicle ahead [47]. In general, such systems monitor the vehicle’s speed and the speed of the vehicle in front of it while measuring the distance between the two vehicles to analyze if there is a risk of collision [48]. Monitoring can be achieved using vision-based sensors, GPS, radar, or LiDAR [49].

Current technology for lane changing focuses primarily on blind-spot identification and warning [50]. The lane-change assistant recognizes vehicles in the blind spot and warns the driver when changing lanes [51]. A lane-departure warning system usually estimates the vehicle’s relative position on the road using a camera to track road markings. The system then provides an alert to prevent an unintended lane departure using audible, visual, or haptic steering wheel feedback [52][53][54]. Such systems have already been rolled out in several commercial vehicles by Volvo, Mercedes, Audi, BMW, Nissan, and Honda [52][53].

Adaptive cruise control (ACC) is an enhancement of the traditional cruise control (CC) system that improves driver convenience, reduces driver workload, and has the potential to improve vehicle safety [55]. Current ACC systems are intelligent systems that control a vehicle’s acceleration and deceleration to maintain pace with the preceding car or travel at the desired speed [55][56]. Such systems are achieved with the data collected from onboard sensors such as an infrared laser, radar, and video sensors [57].

Abnormal driving is usually caused by fatigue, recklessness, and/or drunkenness [58]. A driver with any of these conditions usually exhibits a specific change in behavior or body movement. When a driver is drowsy, they will usually perform actions such as rapid and constant blinking, nodding or swinging their head, and frequent yawning [59]. On the other hand, a drunk driver intoxicated by alcohol usually develops the habit of sudden acceleration or deceleration with a delayed response. Reckless driving is also similar to drunk driving to a certain degree. The driver may be awake but affected by emotional factors, thereby exhibiting sudden acceleration or deceleration and violating the speed limit [58]. Therefore, a driver monitoring system can be achieved by monitoring the driver directly or indirectly. Direct driver monitoring systems include monitoring heart rate and driver body movements using different sensors. Indirect driver monitoring includes analyzing pedal and steering activities and reactions to certain events [60][61]. Upon detecting such abnormal behavior, a warning system will be activated.

3. The Concepts of Autonomous Driving

The designated purpose of a vehicle is to provide transportation from Point A to Point B. Therefore, our review will follow the same sequence of a vehicle moving out of a parking lot/garage, traveling on the road, and parking at the destination.

3.1. Moving Out

On the Internet of Vehicle environment, finding an available parking lot can be solved [62]. This futuristic parking system provides a car park recommendation for the driver while taking the driver’s preferences into account [63][64][65]. Such preferences may include parking fees, distance from the car park to the destination, driving time from the current location to the destination, and reservation reliability [64]. Supporting such a system will require a cooperative network of vehicles, parking lots, and a central server. The car park will first need to provide the correct occupancy status with the precise location and then update it into the central server. Once a driver makes a reservation in that particular car park, information must be updated to the central server. Another possible solution is to book the parking lot in advance through the use of short message service (SMS) [66] or an Android application [67].

For an accurate occupancy status, several authors [68][69][70][71][72] have proposed to use image processing techniques as opposed to using counter-based or sensor-based techniques, which are relatively higher in cost [73]. Sensors deployed to detect occupancy include a passive and an active infrared sensor, an ultrasonic sensor, a magnetometer sensor, and a microwave sensor [74]. Sensors are usually embedded in parking lots to detect the presence of a vehicle. The idea of using the image processing technique is to first capture the parking lot’s image using a camera installed in the car park, followed by using an image processing algorithm to either extract or enhance features in the image. Finally, a classifier is used to classify the occupancy status. Such a system brings the benefits of saving fuel time, as car cruising while waiting for an empty lot is reduced, improving the traffic flow in the car park [75][76]. Furthermore, conflicts between drivers over the rights to the parking lot can be reduced [77].

3.2. On the Road

While traveling on the road, there are several actions that an autonomous vehicle would perform, such as lane keeping, lane changing, overtaking, and obeying traffic rules. For fully autonomous driving, lane keeping is the evolution of a lane-departure warning system. While the idea of detecting whether a vehicle has drifted into another lane unintentionally is the same for both systems, the lane-keeping system corrects the vehicle’s direction to keep it within its lane [78][79]. Early systems corrected vehicle direction by differential braking. Still, current systems actively control steering to maintain lane centering [78] by taking the dynamic and kinematics model of the vehicle into consideration to control the lateral motion of the vehicle [79][80][81][82][83][84]. Such a system is a great tool to prevent off-the-road crashes [85]. Studies have shown a significant reduction in driver injury crashes with cars equipped with such a system compared to cars without this system.

A lane change is described as a maneuver that involves a deliberate and substantial shift in the lateral position when traveling in the same direction associated with simple lane change, merge, exit, pass, and weave maneuvers [86]. Such actions will require a suitable time gap to avoid a frontal collision crash [87]. A lane change is usually carried out due to the following: (1) to travel at a faster lane and (2) to obstacle avoidance. For an autonomous vehicle, other than assessing the lane change risk by checking surrounding vehicles, it should control the vehicle to complete the intended lane change if there is no risk involved [88]. Information such as the position, velocity, and acceleration of surrounding vehicles to generate the risk analysis can be based on computer vision systems, such as those used in lane-departure warning systems, a precise measurement system (inertial navigation aided by a global navigation satellite system (GNSS) in conjunction with high-resolution maps) [89] or a V2V communication system [90]. For normal lane changing, the collision avoidance problem is formulated as an optimization problem where the time for completing the lane changing maneuver is minimized by selecting the appropriate lateral and longitudinal control inputs. After the collection of this information, the vehicle then computes the trajectory of each neighboring vehicle then compares this with its own trajectory to determine if any adjacent vehicle poses a safety risk [89]. In an emergency lane-changing situation such as obstacle avoidance, lateral and longitudinal control inputs are calculated to minimize the longitudinal distance between the vehicle and the obstacle [91]. In such a scenario, geometric characteristics of obstacles are also taken into consideration for the planning of trajectory [92]. Finally, steering control would perform the maneuvering action based on the planned course as closely as possible [92][93]. Such a system will potentially reduce human errors such as inaccurate estimation of the surrounding traffic or illegal maneuvers.

The autonomous overtaking procedure consists of a combination of lane keeping and lane changing. Three consecutive maneuvers could begin with lane changing followed by traveling on a straight path (lane keeping) parallel to the vehicle to overtake and, again, a lane change, which has to be planned and coordinated [94]. This procedure can be divided into two stages. The procedure begins with checking if overtaking can be carried out [95]. Essential factors to consider when performing the maneuvers include the following: a safe distance to the vehicle to be overtaken; an adequate period for each lane-change maneuver, accounting for varying road widths; a smooth and comfortable lane-change trajectory; and safely returning to the original lane or maintaining a safe distance from a vehicle ahead when the overtaking maneuver cannot be executed [96]. Information of surrounding vehicles can be obtained as discussed earlier. The second stage is the performing of the maneuver. The steering control can be achieved through an infrastructure-supported or autonomous approach. The infrastructure-supported system is based on physically or virtually marked trajectories, usually together with V2V communication. In the autonomous overtaking process, only onboard sensors are used to determine the relative position and orientation between vehicles. The vehicle steering control is determined according to the relative position and orientation concerning the vehicle to overtake. Thus, the overtaking vehicle accomplishes the maneuver concerning the overtaken vehicle instead of the road. This system can reduce numerous fatal crashes due to unsafe diversion space from the original lane, poor visibility when passing a vehicle, or erroneous judgment in returning to the lane [96].

By using V2I communication and positioning technology, the traffic controller/roadside infrastructure will have real-time awareness of the number of vehicles, their positions, and their speeds [97]. The autonomous vehicle will communicate with the traffic controller/roadside where information consists of the right of way [97] and allows planning of safe trajectory crossing the intersection [98]. A traffic controller communicates with individual vehicles and assigns specific time slots to pass the intersection after real-time information processing [99]. The controller functions as a virtual traffic light that can change at an infinite frequency [99], enabling intelligent traffic signal phase setting, unlike the current traffic light setting. With the current setting, vehicles are not allowed to cross the intersection if the light corresponding to their lane is red, even in the absence of conflicting vehicles [100]. This causes delay, fuel wastage, tailpipe emissions, and passenger frustration [101][102]. In the nonsignalized intersection where there is no traffic light or other controlling facilities, vehicles first communicate with each other through V2V communication and negotiate intersection passing. Two categories of solutions can be adopted to manage such intersections: a cooperative resource reservation and a trajectory planning approach. The former focuses on scheduling space tiles and time slots requested by vehicles intending to cross the intersection. In contrast, the latter focuses on the relative motion between vehicles to determine a safe crossing sequence [103].

To achieve several functions, as discussed early, an autonomous vehicle’s accurate localization system is required. As classified by Kuutti et al. [6], localization techniques can be categorized by the mapping approach, the sensor-based approach, and cooperative localization techniques. The first method localizes vehicles concerning a reference global or local map. The sensor-based approach utilizes onboard vehicle sensors to find the global position of a vehicle in a specified coordinate system. The primary sensors involved are GPS, inertial motion units (IMUs), cameras, radar, light detection and ranging (LiDAR), and ultrasonic sensors. In the last method, the localization of vehicles is accomplished through broadcasting information of their current state. Through V2V and/or V2I communications, the vehicle can know the surrounding vehicles’ exact location.

3.3. Parking

If a reservation is not made in advance, the autonomous vehicle will assist by using sensors installed to scan for an empty car lot to prevent the driver from driving past an empty lot. Barnes et al. [104] categorized empty lot detection into seven methods: (1) ultrasonic sensor-based, (2) short-range radar sensor-based, (3) image processing, (4) motion stereo-based method, (5) binocular stereo vision-based method, (6) light stripe projection-based method, and (7) scanning laser radar-based. Ultrasonic sensors and radar sensors work in the same way by detecting the distance between the sensor and targeted object based on the data collected to determine the occupancy status [104][105][106][107]. The concept of image processing is similar to car park monitoring, as discussed earlier. The only difference is the camera is installed on the vehicle body. Stereo-based methods first construct a 3D map [108][109] then designate the target position. The light stripe projection-based method recognizes 3D information by analyzing the light stripe made by a light plane projector reflected from objects [110].

Finally, after identifying an empty parking lot, the autonomous vehicle completes the final task by maneuvering itself into the lot. Although there are several different types of parking lots such as parallel, perpendicular, and fishbone parking [111][112], autonomous parking usually consists of two steps: (1) optimal path planning and (2) path following/tracking [112][113][114]. Using data collected from a combination of sensors, the optimal path planning algorithm begins by generating a suitable collision-free path from a given starting point to a required position within the parking lot that satisfies all kinematic constraints. The parking control scheme then adopts a step-by-step control strategy that compares the current position and requires the position to choose the steering action at the current position [115].

Maneuvering a vehicle into a parking spot of limited space is often challenging, especially for novice drivers, and carries the risk of expensive damage [116][117]. Due to this problem, novice drivers are reluctant to try parking in smaller lots and cruise around for another empty lot. This contributes to additional air pollution, fuel consumption, and congestion [114].

References

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. Choi, Y.-J.; Hur, J.; Jeong, H.-Y.; Joo, C. Special issue on V2X communications and networks. J. Commun. Netw. 2017, 19, 205–208.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. 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.
  20. Lee, S.; Kim, D. An energy efficient vehicle to pedestrian communication method for safety applications. Wirel. Pers. Commun. 2016, 86, 1845–1856.
  21. 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.
  22. Campolo, C.; Molinaro, A.; Iera, A.; Menichella, F. 5G network slicing for vehicle-to-everything services. IEEE Wirel. Commun. 2017, 24, 38–45.
  23. 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.
  24. 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.
  25. 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.
  26. 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.
  27. 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.
  28. 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.
  29. 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.
  30. 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.
  31. Machardy, Z.; Khan, A.; Obana, K.; Iwashina, S. V2X access technologies: Regulation, research, and remaining challenges. IEEE Commun. Surv. Tutor. 2018, 20, 1858–1877.
  32. Hu, Q.; Luo, F. Review of secure communication approaches for in-vehicle network. Int. J. Automot. Technol. 2018, 19, 879–894.
  33. 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.
  34. Wang, X.; Mao, S.; Gong, M.X. An overview of 3GPP cellular vehicle-to-everything standards. GetMobile Mob. Comput. Commun. 2017, 21, 19–25.
  35. Chen, L.; Englund, C. Cooperative intersection management: A survey. IEEE Trans. Intell. Transp. Syst. 2016, 17, 570–586.
  36. 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.
  37. 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.
  38. 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.
  39. Mekki, T.; Jabri, I.; Rachedi, A.; ben Jemaa, M. Vehicular cloud networks: Challenges, architectures, and future directions. Veh. Commun. 2017, 9, 268–280.
  40. Boukerche, A.; De Grande, R.E. Vehicular cloud computing: Architectures, applications, and mobility. Comput. Netw. 2018, 135, 171–189.
  41. Yang, Q.; Zhu, B.; Wu, S. An architecture of cloud-assisted information dissemination in vehicular networks. IEEE Access 2016, 4, 2764–2770.
  42. 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.
  43. 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.
  44. 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.
  45. 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.
  46. 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.
  47. 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.
  48. Hubele, N.; Kennedy, K. Forward collision warning system impact. Traffic Inj. Prev. 2018, 19, S78–S83.
  49. 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.
  50. 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.
  51. 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.
  52. 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.
  53. 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.
  54. 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.
  55. 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.
  56. 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.
  57. 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.
  58. Hu, J.; Xu, L.; He, X.; Meng, W. Abnormal driving detection based on normalized driving behavior. IEEE Trans. Veh. Technol. 2017, 66, 6645–6652.
  59. 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.
  60. 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.
  61. 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.
  62. 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.
  63. 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.
  64. 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.
  65. 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.
  66. 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.
  67. 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.
  68. 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.
  69. 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.
  70. 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.
  71. 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.
  72. 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.
  73. 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.
  74. 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.
  75. 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.
  76. 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.
  77. 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.
  78. 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.
  79. 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.
  80. 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.
  81. 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.
  82. 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.
  83. 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.
  84. 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.
  85. Wang, W.; Zhao, D. Evaluation of Lane Departure Correction Systems Using a Regenerative Stochastic Driver Model. IEEE Trans. Intell. Veh. 2017, 2, 221–232.
  86. 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.
  87. 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.
  88. 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.
  89. 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.
  90. 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.
  91. 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.
  92. 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.
  93. Petrov, P.; Nashashibi, F. Modeling and nonlinear adaptive control for autonomous vehicle overtaking. IEEE Trans. Intell. Transp. Syst. 2014, 15, 1643–1656.
  94. Chae, H.; Yi, K. Virtual target-based overtaking decision, motion planning, and control of autonomous vehicles. IEEE Access 2020, 8, 51363–51376.
  95. 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.
  96. 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.
  97. 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.
  98. 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.
  99. Liu, C.; Lin, C.-W.; Shiraishi, S.; Tomizuka, M. Distributed conflict resolution for connected autonomous vehicles. IEEE Trans. Intell. Veh. 2018, 3, 18–29.
  100. Qian, X.; Altché, F.; Grégoire, J.; Fortelle, A. Autonomous intersection management systems: Criteria, implementation and evaluation. IET Intell. Transp. Syst. 2017, 11, 182–189.
  101. Butakov, V.A.; Ioannou, P. Personalized driver assistance for signalized intersections using V2I Communication. IEEE Trans. Intell. Transp. Syst. 2016, 17, 1910–1919.
  102. 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.
  103. 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.
  104. 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.
  105. 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.
  106. 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.
  107. 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.
  108. 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.
  109. 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.
  110. 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.
  111. 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.
  112. 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.
  113. 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.
  114. 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.
  115. 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.
  116. Oetiker, M.; Baker, G.; Guzzella, L. A Navigation-field-based semi-autonomous nonholonomic vehicle-parking assistant. IEEE Trans. Veh. Technol. 2009, 58, 1106–1118.
  117. 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.
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Cheng Siong Chin
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