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Advances in ADAS
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Advances in ADAS

A special issue of World Electric Vehicle Journal (ISSN 2032-6653).

Deadline for manuscript submissions: closed (1 July 2023) | Viewed by 4672

Special Issue Editors

Dr. Zhi Huang

E-Mail Website
Guest Editor
College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China
Interests: autonomous driving; human–-machine cooperation; active safety; optimal control
Dr. Ke Wang

E-Mail Website
Guest Editor
School of the Automobile Engineering, Chongqing University, Chongqing, China
Interests: autonomous vehicle; computer vision

Special Issue Information

Dear Colleagues,

The advanced driving assistance system (ADAS) allows human-driver and automated systems to be in the control loop of the vehicle simultaneously, thus improving traffic safety by compensating for human-driver cognitive ability and physiological limitations. ADAS products are now standard products in the latest manufactured cars.

However, at the current stage, the promotion and large-scale commercialization of ADAS still face some challenges, such as accurate traffic sensing and prediction, human–machine cooperation control, driver intention understanding, driver’s trust in machine intelligence, and ADAS influence on human cognition. These issues have attracted considerable research attention, and many novel achievements have been made to mitigate the research gaps in recent years. This Special Issue aims to present the recent advances and emerging technology in sensing, cooperation, control, and ergonomics issues for ADAS.

Authors are invited to submit original contributions that consider, but are not limited to, the following topics of interest:

  • Trajectory prediction of moving objects;
  • Driver intention understanding;
  • Driver cognitive burden;
  • Driver’s trust in ADAS;
  • Shared and guidance control;
  • Advanced sensing technology;
  • Ethical dilemma;
  • X-by-wire system;
  • Trajectory following control;
  • Experimental methods.

Dr. Zhi Huang
Dr. Ke Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. World Electric Vehicle Journal is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

18 pages, 11563 KiB  
Article
Research on the SSIDM Modeling Mechanism for Equivalent Driver’s Behavior
by Rui Fang
World Electr. Veh. J. 2023, 14(9), 242; https://doi.org/10.3390/wevj14090242 - 1 Sep 2023
Cited by 1 | Viewed by 740
Abstract
To solve the problem of smooth switching between the car-following model and lane-changing model, the Intelligent Driver Model (IDM) for a single lane was used to study the driver’s behavior switching mechanism of normally following, generating intentions to change lanes, creating space and [...] Read more.
To solve the problem of smooth switching between the car-following model and lane-changing model, the Intelligent Driver Model (IDM) for a single lane was used to study the driver’s behavior switching mechanism of normally following, generating intentions to change lanes, creating space and speed gains, and performing lane change. In the case of sufficient lane-changing space and speed gains, the ego vehicle’s intention to change lanes was considered to solve the switching boundary between car-following behavior and lane-changing behavior, which is also the IDM failure point. In the event that there are no lane-changing gains, the IDM was optimized by incorporating the constraint components of the target lane vehicles in conjunction with the actual motion state of the ego vehicle, and the Stepless Switching Intelligent Driver Model (SSIDM) was constructed. Drivers’ natural driving information was collected, and scenario mining was performed on structured roads. On the basis of the collected data, an elliptic equation was used to fit the behavior switching boundary, and the two component balance coefficients of the front and rear vehicles on the target lane were identified. According to the test set verification results, the Mean Square Error (MSE) of the SSIDM is 2.172, which is 57.98% less than that of the conventional single-lane IDM. The SSIDM can accomplish stepless switching comparable to the driver’s behavior between the car-following behavior and the lane-changing behavior, with greater precision than IDM. This research can provide theoretical support for the construction of the point-to-point driving model and the development of L2+ autonomous driving functions. It can provide assistance for the landing and application of full-behavior and full-scene autonomous driving. Full article
(This article belongs to the Special Issue Advances in ADAS)
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18 pages, 9565 KiB  
Article
Quantification and Pictorial Expression of Driving Status Domain Boundaries for Autonomous Vehicles in LTAP/OD Scenarios
by Xuan Ren, Huanhuan Zhang, Xiaolan Wang, Weiwei Zhang and Wangpengfei Yu
World Electr. Veh. J. 2023, 14(7), 187; https://doi.org/10.3390/wevj14070187 - 14 Jul 2023
Viewed by 1080
Abstract
The ability of advanced driver assistance systems (ADAS) and autonomous vehicles to make human-like decisions can be enhanced by providing more detailed information about vehicles and in-vehicle users’ states. In this paper, the driving status domains of vehicles in left turn across path/opposite [...] Read more.
The ability of advanced driver assistance systems (ADAS) and autonomous vehicles to make human-like decisions can be enhanced by providing more detailed information about vehicles and in-vehicle users’ states. In this paper, the driving status domains of vehicles in left turn across path/opposite direction (LTAP/OD) scenarios are subdivided into comfort, discomfort, extreme, and crash, and the boundaries of each status domain are quantified and visualized. First, real unprotected left turn road segments are chosen for the actual vehicle testing. Subjective passenger comfort evaluation results and objective motion state data of vehicles during the experiment are organized and analyzed by statistics. In addition, the pictorials are plotted to determine the comfort and extreme status domain boundaries based on motion state parameters. Second, based on the unprotected left turn kinematic analysis and modeling, as well as a skilled driver risk perception and operational model, the Safe Collision Plots (SCP) of conflicting vehicles in LTAP/OD scenarios are quantified and expressed as pictorial examples. By combining objective motion parameters and passenger experience, intuitively quantifying each driving status domain of vehicles can provide more fine-grained information for the design parameters of ADAS and autonomous vehicles and increase public trust and acceptance of them. Full article
(This article belongs to the Special Issue Advances in ADAS)
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16 pages, 1360 KiB  
Article
Learning-Based Model Predictive Control for Autonomous Racing
by João Pinho, Gabriel Costa, Pedro U. Lima and Miguel Ayala Botto
World Electr. Veh. J. 2023, 14(7), 163; https://doi.org/10.3390/wevj14070163 - 21 Jun 2023
Viewed by 2405
Abstract
In this paper, we present the adaptation of the terminal component learning-based model predictive control (TC-LMPC) architecture for autonomous racing to the Formula Student Driverless (FSD) context. We test the TC-LMPC architecture, a reference-free controller that is able to learn from previous iterations [...] Read more.
In this paper, we present the adaptation of the terminal component learning-based model predictive control (TC-LMPC) architecture for autonomous racing to the Formula Student Driverless (FSD) context. We test the TC-LMPC architecture, a reference-free controller that is able to learn from previous iterations by building an appropriate terminal safe set and terminal cost from collected trajectories and input sequences, in a vehicle simulator dedicated to the FSD competition. One major problem in autonomous racing is the difficulty in obtaining accurate highly nonlinear vehicle models that cover the entire performance envelope. This is more severe as the controller pushes for incrementally more aggressive behavior. To address this problem, we use offline and online measurements and machine learning (ML) techniques for the online adaptation of the vehicle model. We test two sparse Gaussian process regression (GPR) approximations for model learning. The novelty in the model learning segment is the use of a selection method for the initial training dataset that maximizes the information gain criterion. The TC-LMPC with model learning achieves a 5.9 s reduction (3%) in the total 10-lap FSD race time. Full article
(This article belongs to the Special Issue Advances in ADAS)
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