Connected, Autonomous Driving and Smart Transportation: Theory, Methodology, Simulation and Optimization

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 4589

Special Issue Editors


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Guest Editor
School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, China
Interests: internet of vehicles; automatic driving; smart transportation

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Guest Editor
School of Transportation Science and Engineering, Beihang University, Beijing 100083, China
Interests: intelligent transportation system; vehicle cooperative control; edge computing

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Guest Editor
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100091, China
Interests: traffic perception and big data; Intelligent traffic control and optimization; traffic system simulation and test

Special Issue Information

Dear Colleagues,

Connected and autonomous driving(CAD)combines wireless communication, intelligent driving, and artificial intelligence technology to realize dynamic information collection and real-time interaction with vehicles and infrastructure, as well as providing functions, such as driving environment perception, vehicle autonomous control, and traffic cooperative control. The CAD technology provides the foundation for the smart road transportation system to enhance traffic safety, improve traffic efficiency, and reduce environmental pollution. CAD is important for solving many traffic problems, which has developed rapidly worldwide in recent years. However, with the improvement of the CAD level and the increase of traffic complexity, challenges have arisen in implementing a compatible and scalable smart system, which are essential to overcome to reach an efficient, safe, green, and smart road transportation system. Therefore, it will be of great theoretical significance and practical value to study the emerging methods and technologies in connected autonomous driving and smart transportation.

The aim of this Special Issue is to collate original research and surveys on connected autonomous driving and smart transportation. We welcome both original research and review articles.

Potential topics include, but are not limited to, the following:

  • Perception, prediction, positioning, and navigation methods in connected and autonomous vehicles;
  • Trajectory optimization and control of connected and autonomous vehicles;
  • Advanced driving assistance system;
  • Cooperative management and control methods of connected and autonomous vehicles;
  • Modeling and control methods of mixed traffic flow;
  • Connected and autonomous driving simulation and test methods;
  • AI-enabled, edge-based, and big data methods in smart transportation.

Prof. Dr. Fei Hui
Prof. Dr. Daxin Tian
Prof. Dr. Wei Shangguan
Guest Editors

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Keywords

  • connected and autonomous driving technology
  • smart transportation
  • computer vision
  • artificial intelligence technology
  • edge computing
  • vehicle cooperative control
  • mixed traffic flow
  • connected and autonomous vehicles test
  • big data
  • advanced driving assistance system

Published Papers (5 papers)

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Research

15 pages, 1541 KiB  
Article
An Improved Advanced Driver-Assistance System: Model-Free Prescribed Performance Adaptive Cruise Control
by Peilun Ju and Jiacheng Song
Appl. Sci. 2023, 13(20), 11499; https://doi.org/10.3390/app132011499 - 20 Oct 2023
Cited by 1 | Viewed by 599
Abstract
To maintain a safe distance between the autonomous vehicle and the leader, ensure that the vehicle runs at its expected speed as far as possible, and achieve various control requirements such as speed, distance and collision avoidance, a model-free prescribed performance adaptive cruise [...] Read more.
To maintain a safe distance between the autonomous vehicle and the leader, ensure that the vehicle runs at its expected speed as far as possible, and achieve various control requirements such as speed, distance and collision avoidance, a model-free prescribed performance adaptive cruise control (ACC) algorithm based on funnel control is proposed. The contributions of this paper are that the designed ACC algorithm only requires the speed and position information and can constrain their tracking errors within a predetermined range. When the follower is far away from the leader, the speed-prescribed performance controller adjusts the follower vehicle’s speed to the reference velocity. When the follower vehicle approaches the leader vehicle, a distance-prescribed performance controller is designed to adjust the distance between the follower and the leader. On this basis, the prescribed performance function can expand the switching interval, thereby improving the robustness of the speed and distance control switching process. The effectiveness of the designed algorithm is demonstrated in three scenarios, such as approaching and following, emergency braking, and frequent starting and stopping. The results show that during the speed control stage, the designed algorithm allows the vehicle’s operating speed to vary within a predetermined spatial range; in the distance control stage, the designed algorithm strictly limits the distance error within the preset range. The speed and distance of the vehicle change smoothly, and there is no overshoot during the initial state adjustment, emergency braking, and frequent start and stop stages, demonstrating a good control effect. Full article
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21 pages, 3585 KiB  
Article
Analysis of Carbon Emissions in Heterogeneous Traffic Flow within the Influence Area of Highway Off-Ramps
by Xiaozhi Su, Fangrong Chen, Bowei Li, Liangchen Liu and Yun Xiang
Appl. Sci. 2023, 13(17), 9554; https://doi.org/10.3390/app13179554 - 23 Aug 2023
Viewed by 762
Abstract
With the continuous advancements in electrification, connectivity, and intelligence in the automotive industry, the mixed traffic of vehicles with different levels of driving automation is changing the carbon emission characteristics in the impact areas of off-ramps on highways. Considering the insufficient research on [...] Read more.
With the continuous advancements in electrification, connectivity, and intelligence in the automotive industry, the mixed traffic of vehicles with different levels of driving automation is changing the carbon emission characteristics in the impact areas of off-ramps on highways. Considering the insufficient research on the carbon emission characteristics of heterogeneous traffic flow in the downstream influence areas of highway off-ramps, this study applied a scenario analysis method. Furthermore, considering factors such as vehicle composition, road control, and platoon management, it establishes and calibrates measurement models for carbon emissions from conventional vehicles, intelligent vehicles, the platoon driving of electric vehicles, and the mixed platoon driving of conventional vehicles and electric vehicles. This study also provides a simulation scenario for a three-lane highway off-ramp based on the actual conditions of the Xi’an Ring Expressway. Finally, by applying the constructed carbon emission calculation models for heterogeneous traffic flow in the intelligent vehicle mixed traffic scenario, a quantitative analysis was conducted to assess the impacts of the intelligent vehicle infiltration rate, off-ramp vehicle proportion, smart-vehicle-dedicated lanes, and platoon driving on carbon emissions in the downstream influence area of off-ramps. The results revealed the impact of intelligent vehicle integration and platoon driving on carbon emission characteristics in the downstream influence areas of highway off-ramps. Full article
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21 pages, 17721 KiB  
Article
Energy Management Strategy Based on V2X Communications and Road Information for a Connected PHEV and Its Evaluation Using an IDHIL Simulator
by Seongmin Ha and Hyeongcheol Lee
Appl. Sci. 2023, 13(16), 9208; https://doi.org/10.3390/app13169208 - 13 Aug 2023
Viewed by 922
Abstract
Conventional energy management strategies (EMSs) of hybrid electric vehicles (HEVs) only utilize in-vehicle information, such as an acceleration pedal, velocity, acceleration, engine RPM, state of charge (SOC), and radar. This paper presents a new EMS using out-vehicle information obtained by vehicle to everything [...] Read more.
Conventional energy management strategies (EMSs) of hybrid electric vehicles (HEVs) only utilize in-vehicle information, such as an acceleration pedal, velocity, acceleration, engine RPM, state of charge (SOC), and radar. This paper presents a new EMS using out-vehicle information obtained by vehicle to everything (V2X) communication. The new EMS integrates cooperative eco-driving (CED) guidance and an adaptive equivalent consumption minimum strategy (A-ECMS) based on V2X communication information and road information. CED provides a guide signal and a guide speed to the driver. It guides pedal behavior in terms of coasting driving, acceleration and deceleration, and target speed. A-ECMSs calculate the target SOC based on the simplified road information of the planned route and reflects it in the equivalent factor. An integrated driving hardware-in-the-loop (IDHIL) simulator is also built to prove the new EMS by integrating a V2X communication device, a VANET simulator, and a vehicle simulator. The IDHIL test results demonstrate the validity and performance of the proposed EMS in a V2X communication environment. Full article
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14 pages, 1583 KiB  
Article
Cargo Terminal Intelligent-Scheduling Strategies Based on Improved Bee Colony Algorithms
by Haiquan Wang, Menghao Su, Xiaobin Xu, Hans-Dietrich Haasis, Ran Zhao, Shengjun Wen and Yan Wang
Appl. Sci. 2023, 13(15), 8750; https://doi.org/10.3390/app13158750 - 28 Jul 2023
Viewed by 650
Abstract
Due to the rapid increase in cargoes and postal transport volumes in smart transportation systems, an efficient automated multidimensional terminal with autonomous elevating transfer vehicles (ETVs) should be established, and an effective cooperative scheduling strategy for vehicles needs to be designed for improving [...] Read more.
Due to the rapid increase in cargoes and postal transport volumes in smart transportation systems, an efficient automated multidimensional terminal with autonomous elevating transfer vehicles (ETVs) should be established, and an effective cooperative scheduling strategy for vehicles needs to be designed for improving cargo handling efficiency. In this paper, as one of the most effective artificial intelligence technologies, the artificial bee colony algorithm (ABC), which possesses a strong global optimization ability and fewer parameters, is firstly introduced to simultaneously manage the autonomous ETVs and assign the corresponding entrances and exits. Moreover, as ABC has the disadvantage of slow convergence rate, a novel full-dimensional search strategy with parallelization (PfdABC) and a random multidimensional search strategy (RmdABC) are incorporated in the framework of ABC to increase the convergence speed. After being evaluated on benchmark functions, it is applied to solve the combinatorial optimization problem with multiple tasks and multiple entrances and exits in the terminal. The simulations show that the proposed algorithms can achieve a much more desired performance than the traditional artificial bee colony algorithm in terms of balancing the exploitation and exploration abilities, especially when dealing with the cooperative control and scheduling problems. Full article
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19 pages, 2426 KiB  
Article
Optimal Driving Model for Connected and Automated Electric Freight Vehicles in a Wireless Charging Scenario at Signalised Intersections
by Wenbo Wang, Songhua Fan, Zijian Wang, Xinpeng Yao and Kenan Mu
Appl. Sci. 2023, 13(10), 6286; https://doi.org/10.3390/app13106286 - 21 May 2023
Viewed by 846
Abstract
Electric freight vehicles have become an important means of transportation in connected and automated environments owing to their numerous advantages. However, the generally short driving range of connected and automated electric freight vehicles (CAEFVs) does not satisfy the growing transport demand. In this [...] Read more.
Electric freight vehicles have become an important means of transportation in connected and automated environments owing to their numerous advantages. However, the generally short driving range of connected and automated electric freight vehicles (CAEFVs) does not satisfy the growing transport demand. In this study, wireless charging technology is employed to construct a complex driving scenario including urban roads and dynamic wireless charging facilities. A combination of variable-scale elements consisting of vehicles, roads, and the environment is analysed hierarchically to develop a wireless charging scheme for urban transport systems. Using passage efficiency, energy consumption, and passenger comfort as the joint optimisation objectives, an optimal driving model for CAEFVs in wireless charging scenarios at signalised intersections combining scenario boundaries and vehicle dynamic constraints is proposed. Considering the differentiated charging needs of vehicles, this model is divided into a time priority strategy (TPS), balance priority strategy (BPS), and charging priority strategy (CPS). The obtained results reveal that the CPS is superior to the TPS in terms of the charging benefits but requires a longer travel time. Meanwhile, the BPS increases the charging benefits and passing efficiency. This study provides guidance for the deployment of wireless charging lanes with a high application value. Full article
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