AI-Empowered Assisted and Autonomous Driving

A special issue of Vehicles (ISSN 2624-8921).

Deadline for manuscript submissions: 30 October 2025 | Viewed by 447

Special Issue Editors


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Guest Editor
School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China
Interests: train energy-efficient control; driver advisory system; train scheduling and control; utilization of regenerative braking energy

E-Mail Website
Guest Editor
School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China
Interests: train energy-efficient control; maglev train control

Special Issue Information

Dear Colleagues,

The fast-paced development of AI technologies provides significant opportunities for vehicle intelligence. AI-empowered assisted and autonomous driving technologies are the latest developments in transportation. These advancements can redefine the mobility, safety, and efficiency of the transportation system, offering a glimpse into a world where human error, a leading cause of traffic accidents, is significantly reduced.
Assisted driving, also known as semi-autonomous driving, leverages AI to enhance driver capabilities and vehicle performance. AI systems in these vehicles function as co-pilots, providing real-time assistance to the driver through Advanced Driver-Assistance Systems (ADASs). 
 
Autonomous driving, on the other hand, takes the concept of AI in vehicles to the next level. Fully autonomous vehicles, also known as self-driving vehicles, operate without the need for human intervention.

At the brink of this technological revolution, AI-empowered assisted and autonomous driving will transform our transportation networks. The marriage of AI and automotive technology is not only about convenience but about creating a safer, more efficient, and more inclusive mobility ecosystem for the future.

Dr. Xubin Sun
Dr. Weifeng Zhong
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.

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Keywords

  • intelligent train control
  • ADAS
  • autonomous driving
  • enforcement learning
  • deep learning
  • energy saving

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Published Papers (1 paper)

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Research

14 pages, 1881 KiB  
Article
Optimization of Adaptive Cruise Control Strategies Based on the Responsibility-Sensitive Safety Model
by Tengwei Yu, Yubin Tang, Renxiang Chen and Shuen Zhao
Vehicles 2025, 7(2), 28; https://doi.org/10.3390/vehicles7020028 - 26 Mar 2025
Viewed by 282
Abstract
The collision avoidance capability of autonomous vehicles in extreme traffic conditions remains a focal point of research. This paper introduces an Adaptive Cruise Control (ACC) strategy based on Model Predictive Control (MPC) and Responsibility-Sensitive Safety (RSS) models. Simulations were conducted in the CARLA [...] Read more.
The collision avoidance capability of autonomous vehicles in extreme traffic conditions remains a focal point of research. This paper introduces an Adaptive Cruise Control (ACC) strategy based on Model Predictive Control (MPC) and Responsibility-Sensitive Safety (RSS) models. Simulations were conducted in the CARLA environment, where the lead vehicle underwent various rapid deceleration scenarios to optimize the following vehicle’s braking strategy. By integrating the multi-step predictive optimization capabilities of MPC with the dynamic safety assessment mechanisms of RSS, the proposed strategy ensures safe following distances while achieving rapid and precise speed adjustments, thereby enhancing the system’s responsiveness and safety. The model also incorporates a secondary optimization to balance comfort and stability, thereby improving the overall performance of autonomous vehicles. The use of multi-dimensional assessment metrics, such as Time to Collision (TTC), Time Exposed TTC (TET), and Time Integrated TTC (TIT), addresses the limitations of using TTC alone, which only reflects instantaneous collision risk. The optimization of the model in this paper aims to improve the safety and comfort of the following vehicle in scenarios with various gap distances, and it has been validated through the SSM multi-indicator approach. Experimental results demonstrate that the improved ACC model significantly enhances vehicle safety and comfort in scenarios involving large gaps and short-distance emergency braking by the lead vehicle, validating the method’s effectiveness in various extreme traffic scenarios. Full article
(This article belongs to the Special Issue AI-Empowered Assisted and Autonomous Driving)
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