Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
AI-Driving for Autonomous Vehicles
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

AI-Driving for Autonomous Vehicles

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 4335

Special Issue Editors

Prof. Dr. Agapito Ledezma Espino

E-Mail Website
Guest Editor
Computer Science and Engineering Department, Universidad Carlos III de Madrid, 28911 Leganés (Madrid), Spain
Interests: ADAS; autonomous vehicles; AI4D
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Araceli Sanchis de Miguel

E-Mail Website
Guest Editor
Computer Science and Engineering Department, Universidad Carlos III de Madrid, 28911 Leganés (Madrid), Spain
Interests: computer science; Artificial Intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is probably the most disruptive technology in development today. The application areas of AI are limitless, and its application is significantly changing the modus operandi in many industries. Just as mass production and the assembly line revolutionized the automotive industry in the second industrial revolution, AI is revolutionizing everything related to vehicles today. This is the case in the automotive industry, where AI is present from manufacturing to after-sales services. From advanced driving assistance systems to autonomous cars, AI constitutes a fundamental piece in the development of the automotive industry.

The disruption caused by AI is not limited to autonomous cars but also to all types of vehicles. Hence, we find examples of aerial, aquatic, underwater, and other land vehicles with different degrees of autonomy. Despite the progress the has been made in recent decades, autonomous driving is an area that is constantly evolving and is still an open topic.

In this Special Issue, we are particularly interested in advances related to the application of AI in autonomous vehicle driving. We encourage researchers working in relevant areas to submit conceptual and empirical articles, in addition to literature review papers, on this topic.

Possible topics of interest include, but are not limited to:

  • Computer vision for objects detection.
  • Machine learning for autonomous driving (e.g., deep learning, reinforcement learning).
  • Sensor fusion for autonomous driving.
  • HMI for autonomous driving.
  • Explainable AI.
  • Safety and Ethical aspects.
  • Simulation for autonomous driving.

Prof. Dr. Agapito Ledezma Espino
Prof. Dr. Araceli Sanchis de Miguel
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. Sensors 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 2600 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

  • detection algorithms
  • autopilots
  • connected cars
  • self-driving
  • passengers’ experiences

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 12925 KiB  
Article
Learning-Based Control of Autonomous Vehicles Using an Adaptive Neuro-Fuzzy Inference System and the Linear Matrix Inequality Approach
by Mohammad Sheikhsamad and Vicenç Puig
Sensors 2024, 24(8), 2551; https://doi.org/10.3390/s24082551 - 16 Apr 2024
Viewed by 300
Abstract
This paper proposes a learning-based control approach for autonomous vehicles. An explicit Takagi–Sugeno (TS) controller is learned using input and output data from a preexisting controller, employing the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm. At the same time, the vehicle model is identified [...] Read more.
This paper proposes a learning-based control approach for autonomous vehicles. An explicit Takagi–Sugeno (TS) controller is learned using input and output data from a preexisting controller, employing the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm. At the same time, the vehicle model is identified in the TS model form for closed-loop stability assessment using Lyapunov theory and LMIs. The proposed approach is applied to learn the control law from an MPC controller, thus avoiding the use of online optimization. This reduces the computational burden of the control loop and facilitates real-time implementation. Finally, the proposed approach is assessed through simulation using a small-scale autonomous racing car. Full article
(This article belongs to the Special Issue AI-Driving for Autonomous Vehicles)
►▼ Show Figures

Figure 1

17 pages, 1135 KiB  
Article
Goal-Guided Graph Attention Network with Interactive State Refinement for Multi-Agent Trajectory Prediction
by Jianghang Wu, Senyao Qiao, Haocheng Li, Boyu Sun, Fei Gao, Hongyu Hu and Rui Zhao
Sensors 2024, 24(7), 2065; https://doi.org/10.3390/s24072065 - 23 Mar 2024
Viewed by 587
Abstract
The accurate prediction of the future trajectories of traffic participants is crucial for enhancing the safety and decision-making capabilities of autonomous vehicles. Modeling social interactions among agents and revealing the inherent relationships is crucial for accurate trajectory prediction. In this context, we propose [...] Read more.
The accurate prediction of the future trajectories of traffic participants is crucial for enhancing the safety and decision-making capabilities of autonomous vehicles. Modeling social interactions among agents and revealing the inherent relationships is crucial for accurate trajectory prediction. In this context, we propose a goal-guided and interaction-aware state refinement graph attention network (SRGAT) for multi-agent trajectory prediction. This model effectively integrates high-precision map data and dynamic traffic states and captures long-term temporal dependencies through the Transformer network. Based on these dependencies, it generates multiple potential goals and Points of Interest (POIs). Through its dual-branch, multimodal prediction approach, the model not only proposes various plausible future trajectories associated with these POIs, but also rigorously assesses the confidence levels of each trajectory. This goal-oriented strategy enables SRGAT to accurately predict the future movement trajectories of other vehicles in complex traffic scenarios. Tested on the Argoverse and nuScenes datasets, SRGAT surpasses existing algorithms in key performance metrics by adeptly integrating past trajectories and current context. This goal-guided approach not only enhances long-term prediction accuracy, but also ensures its reliability, demonstrating a significant advancement in trajectory forecasting. Full article
(This article belongs to the Special Issue AI-Driving for Autonomous Vehicles)
►▼ Show Figures

Figure 1

20 pages, 6982 KiB  
Article
Learn to Bet: Using Reinforcement Learning to Improve Vehicle Bids in Auction-Based Smart Intersections
by Giacomo Cabri, Matteo Lugli, Manuela Montangero and Filippo Muzzini
Sensors 2024, 24(4), 1288; https://doi.org/10.3390/s24041288 - 17 Feb 2024
Viewed by 485
Abstract
With the advent of IoT, cities will soon be populated by autonomous vehicles and managed by intelligent systems capable of actively interacting with city infrastructures and vehicles. In this work, we propose a model based on reinforcement learning that teaches to autonomous connected [...] Read more.
With the advent of IoT, cities will soon be populated by autonomous vehicles and managed by intelligent systems capable of actively interacting with city infrastructures and vehicles. In this work, we propose a model based on reinforcement learning that teaches to autonomous connected vehicles how to save resources while navigating in such an environment. In particular, we focus on budget savings in the context of auction-based intersection management systems. We trained several models with Deep Q-learning by varying traffic conditions to find the most performance-effective variant in terms of the trade-off between saved currency and trip times. Afterward, we compared the performance of our model with previously proposed and random strategies, even under adverse traffic conditions. Our model appears to be robust and manages to save a considerable amount of currency without significantly increasing the waiting time in traffic. For example, the learner bidder saves at least 20% of its budget with heavy traffic conditions and up to 74% in lighter traffic with respect to a standard bidder, and around three times the saving of a random bidder. The results and discussion suggest practical adoption of the proposal in a foreseen future real-life scenario. Full article
(This article belongs to the Special Issue AI-Driving for Autonomous Vehicles)
►▼ Show Figures

Figure 1

17 pages, 4414 KiB  
Article
Coordinated Decision Control of Lane-Change and Car-Following for Intelligent Vehicle Based on Time Series Prediction and Deep Reinforcement Learning
by Kun Zhang, Tonglin Pu, Qianxi Zhang and Zhigen Nie
Sensors 2024, 24(2), 403; https://doi.org/10.3390/s24020403 - 09 Jan 2024
Viewed by 680
Abstract
Adaptive cruise control and autonomous lane-change systems represent pivotal advancements in intelligent vehicle technology. To enhance the operational efficiency of intelligent vehicles in combined lane-change and car-following scenarios, we propose a coordinated decision control model based on hierarchical time series prediction and deep [...] Read more.
Adaptive cruise control and autonomous lane-change systems represent pivotal advancements in intelligent vehicle technology. To enhance the operational efficiency of intelligent vehicles in combined lane-change and car-following scenarios, we propose a coordinated decision control model based on hierarchical time series prediction and deep reinforcement learning under the influence of multiple surrounding vehicles. Firstly, we analyze the lane-change behavior and establish boundary conditions for safe lane-change, and divide the lane-change trajectory planning problem into longitudinal velocity planning and lateral trajectory planning. LSTM network is introduced to predict the driving states of surrounding vehicles in multi-step time series, combining D3QN algorithm to make decisions on lane-change behavior. Secondly, based on the following state between the ego vehicle and the leader vehicle in the initial lane, as well as the relationship between the initial distance and the expected distance with the leader vehicle in the target lane, with the primary objective of maximizing driving efficiency, longitudinal velocity is planned based on driving conditions recognition. The lateral trajectory and conditions recognition are then planned using the GA-LSTM-BP algorithm. In contrast to conventional adaptive cruise control systems, the DDPG algorithm serves as the lower-level control model for car-following, enabling continuous velocity control. The proposed model is subsequently simulated and validated using the NGSIM dataset and a lane-change scenarios dataset. The results demonstrate that the algorithm facilitates intelligent vehicle lane-change and car-following coordinated control while ensuring safety and stability during lane-changes. Comparative analysis with other decision control models reveals a notable 17.58% increase in driving velocity, underscoring the algorithm’s effectiveness in improving driving efficiency. Full article
(This article belongs to the Special Issue AI-Driving for Autonomous Vehicles)
►▼ Show Figures

Figure 1

15 pages, 5807 KiB  
Article
Performance Verification of Autonomous Driving LiDAR Sensors under Rainfall Conditions in Darkroom
by Jaeryun Choe, Hyunwoo Cho and Yoonseok Chung
Sensors 2024, 24(1), 14; https://doi.org/10.3390/s24010014 - 19 Dec 2023
Viewed by 770
Abstract
This research aims to assess the functionality of the VLP-32 LiDAR sensor, which serves as the principal sensor for object recognition in autonomous vehicles. The evaluation is conducted by simulating edge conditions the sensor might encounter in a controlled darkroom setting. Parameters for [...] Read more.
This research aims to assess the functionality of the VLP-32 LiDAR sensor, which serves as the principal sensor for object recognition in autonomous vehicles. The evaluation is conducted by simulating edge conditions the sensor might encounter in a controlled darkroom setting. Parameters for environmental conditions under examination encompass measurement distances ranging from 10 to 30 m, varying rainfall intensities (0, 20, 30, 40 mm/h), and different observation angles (0°, 30°, 60°). For the material aspects, the investigation incorporates reference materials, traffic signs, and road surfaces. Employing this diverse set of conditions, the study quantitatively assesses two critical performance metrics of LiDAR: intensity and NPC (number of point clouds). The results indicate a general decline in intensity as the measurement distance, rainfall intensity, and observation angles increase. Instances were identified where the sensor failed to record intensity for materials with low reflective properties. Concerning NPC, both the effective measurement area and recorded values demonstrated a decreasing trend with enlarging measurement distance and angles of observation. However, NPC metrics remained stable despite fluctuations in rainfall intensity. Full article
(This article belongs to the Special Issue AI-Driving for Autonomous Vehicles)
►▼ Show Figures

Figure 1

27 pages, 6199 KiB  
Article
End-to-End Autonomous Navigation Based on Deep Reinforcement Learning with a Survival Penalty Function
by Shyr-Long Jeng and Chienhsun Chiang
Sensors 2023, 23(20), 8651; https://doi.org/10.3390/s23208651 - 23 Oct 2023
Cited by 4 | Viewed by 1020
Abstract
An end-to-end approach to autonomous navigation that is based on deep reinforcement learning (DRL) with a survival penalty function is proposed in this paper. Two actor–critic (AC) frameworks, namely, deep deterministic policy gradient (DDPG) and twin-delayed DDPG (TD3), are employed to enable a [...] Read more.
An end-to-end approach to autonomous navigation that is based on deep reinforcement learning (DRL) with a survival penalty function is proposed in this paper. Two actor–critic (AC) frameworks, namely, deep deterministic policy gradient (DDPG) and twin-delayed DDPG (TD3), are employed to enable a nonholonomic wheeled mobile robot (WMR) to perform navigation in dynamic environments containing obstacles and for which no maps are available. A comprehensive reward based on the survival penalty function is introduced; this approach effectively solves the sparse reward problem and enables the WMR to move toward its target. Consecutive episodes are connected to increase the cumulative penalty for scenarios involving obstacles; this method prevents training failure and enables the WMR to plan a collision-free path. Simulations are conducted for four scenarios—movement in an obstacle-free space, in a parking lot, at an intersection without and with a central obstacle, and in a multiple obstacle space—to demonstrate the efficiency and operational safety of our method. For the same navigation environment, compared with the DDPG algorithm, the TD3 algorithm exhibits faster numerical convergence and higher stability in the training phase, as well as a higher task execution success rate in the evaluation phase. Full article
(This article belongs to the Special Issue AI-Driving for Autonomous Vehicles)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop