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Applied Sciences
Special Issues
Trends and Prospects in Intelligent Automotive Systems
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Trends and Prospects in Intelligent Automotive Systems

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

Deadline for manuscript submissions: 20 September 2024 | Viewed by 1959

Special Issue Editor

Dr. Zhiheng Li

E-Mail Website
Guest Editor
Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
Interests: intelligent transportation system; electric vehicles

Special Issue Information

Dear Colleagues,

Transportation systems play important roles in modern society, especially during the process of urbanization. In recent years, new technologies, including artificial intelligence (AI) and big data, have promoted the rapid development of intelligent transportation systems. Methodologies involving design, modeling, control and testing are emerging, and such innovations greatly improve the efficiency and safety of existing transportation systems.

Therefore, this Special Issue aims to gather research advances in intelligent transportation systems. Potential topics include but are not limited to:

  • Machine learning;
  • Advanced control algorithm;
  • Advanced data processing;
  • Simulation;
  • System integration and testing.

Dr. Zhiheng Li
Guest Editor

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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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.

Keywords

  • intelligent transportation system
  • artificial intelligence
  • advanced controlling
  • system testing
  • simulation experiments

Published Papers (2 papers)

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Research

23 pages, 6323 KiB  
Article
Energy-Efficient Lane Change Trajectory Planning for Highway Traffic Scenarios Considering Different Driving Needs
by Rui Song, Xinfeng Zhang, Haojie Zhang, Yiheng Dai, Yuxuan Zhu and Shengzhe Tian
Appl. Sci. 2023, 13(24), 13184; https://doi.org/10.3390/app132413184 - 12 Dec 2023
Viewed by 592
Abstract
This paper proposes an energy-saving lane-changing trajectory model for intelligent electric vehicles at high speeds that considers different driving needs under multiple constraints to address the issues of simple lane-changing considerations and poor safety. An economic index is added to construct a multi-objective [...] Read more.
This paper proposes an energy-saving lane-changing trajectory model for intelligent electric vehicles at high speeds that considers different driving needs under multiple constraints to address the issues of simple lane-changing considerations and poor safety. An economic index is added to construct a multi-objective optimization function based on the comfort, safety, and efficiency of lane changing. The particle swarm optimization algorithm is used to solve the optimal lane-changing time. The weight of the index is obtained by analyzing different driving needs using the Analytic Hierarchy Process in different scenarios. The multi-objective function is adjusted to plan the optimal lane-changing trajectory that meets driving needs. The simulation shows that the proposed model can generate smooth and feasible lane-changing trajectories that meet different driving needs. The energy consumption analysis results indicate that the construction of economic indicators can effectively reduce the energy consumption of vehicles driving on highways. The tracking analysis results indicate that the target vehicle can smoothly and safely change lanes on the planned trajectory, verifying the effectiveness and rationality of the planned trajectory. Full article
(This article belongs to the Special Issue Trends and Prospects in Intelligent Automotive Systems)
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16 pages, 1451 KiB  
Article
A Quantitative Approach of Generating Challenging Testing Scenarios Based on Functional Safety Standard
by Kang Meng, Rui Zhou, Zhiheng Li and Kai Zhang
Appl. Sci. 2023, 13(6), 3494; https://doi.org/10.3390/app13063494 - 09 Mar 2023
Cited by 1 | Viewed by 981
Abstract
With the rapid development of intelligent vehicle safety verification, scenario-based testing methods have received increasing attention. As the space of driving scenarios is vast, the challenge in scenario-based testing is the generation and selection of high-value testing scenarios to reduce the development and [...] Read more.
With the rapid development of intelligent vehicle safety verification, scenario-based testing methods have received increasing attention. As the space of driving scenarios is vast, the challenge in scenario-based testing is the generation and selection of high-value testing scenarios to reduce the development and validation time. This paper proposes a method for generating challenging test scenarios. Our method quantifies the challenges in these scenarios by estimating the risks based on ISO 26262. We formulate the problem as a Markov decision process and quantify the challenges in the current state using the three risk factors provided in ISO 26262: exposure, severity, and controllability. We then employ reinforcement learning algorithms to identify the challenges and use the state–action value matrix to select motions for a background vehicle to generate critical scenarios. The effectiveness of the approach is validated by testing the generated challenge scenarios using a simulation model. The results show that our method can ensure both accuracy and coverage, and the larger the state space is, the more accident-prone the generated scenarios are. Our proposed method is general and easily adaptable to other cases. Full article
(This article belongs to the Special Issue Trends and Prospects in Intelligent Automotive Systems)
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