Autonomous Vehicles Technological Trends

Talavera, Edgar; Díaz-Álvarez, Alberto; Naranjo, José Eugenio; Olaverri-Monreal, Cristina

doi:10.3390/electronics10101207

Cited by 7 publications

(4 citation statements)

References 32 publications

(35 reference statements)

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“…In our article, we are looking for the traffic node, which is completely provable and consistent if (9) 0 < K < ∞…”

Section: Kolmogorov Entropy Methodsmentioning

confidence: 99%

“…To bring them into our everyday lives today, both new, more accurate sensors [4] and new research in artificial intelligence [5] are needed. Some research has focused on autonomous cruise control for remotely piloted ships [6] and decision-making related to maritime self-driving navigation [7] or autonomous underwater vehicles [8], but in this paper, we focus on road transport, which is a much more challenging area for researchers due to the complexity of traffic situations [9,10]. More and more researchers are taking up research in this complex area.…”

Section: Introductionmentioning

confidence: 99%

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Using Kolmogorov Entropy to Verify the Description Completeness of Traffic Dynamics of Highly Autonomous Driving

Kiss,

Bakucz

2024

Applied Sciences

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In this paper, we outline the analysis of a fully provable traffic system based on the Kolmogorov entropy. The completeness of the traffic node dynamics is realized in the form of a nonlinear dynamical model of the participating transport objects. The goal of this study is to determine the completeness of transport nodes based on the Kolmogorov entropy of the traffic trajectories of a node with an unspecified number of actors, like cars and pedestrians. The completeness of a highly autonomous driving detection system describing a traffic node could be realized if the entropy-based error-doubling time of the trajectories of the Euler–Lagrange equation interpreted at the transport junction is less than 1.3.

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“…In our article, we are looking for the traffic node, which is completely provable and consistent if (9) 0 < K < ∞…”

Section: Kolmogorov Entropy Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Kolmogorov Entropy to Verify the Description Completeness of Traffic Dynamics of Highly Autonomous Driving

Kiss,

Bakucz

2024

Applied Sciences

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“…In recent years, the rapid development of artificial intelligence has driven the technological progress of self-driving vehicles and intelligent transportation systems. Autonomous vehicles are one of the core components of intelligent transportation systems with increasing safety and reliability requirements [1][2][3][4]. Path planning and tracking control are two core issues in the autonomous driving system.…”

Section: Introductionmentioning

confidence: 99%

Research on Path Planning and Tracking Control of Autonomous Vehicles Based on Improved RRT* and PSO-LQR

2023

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Path planning and tracking control are essential parts of autonomous vehicle research. Regarding path planning, the Rapid Exploration Random Tree Star (RRT*) algorithm has attracted much attention due to its completeness. However, the algorithm still suffers from slow convergence and high randomness. Regarding path tracking, the Linear Quadratic Regulator (LQR) algorithm is widely used in various control applications due to its efficient stability and ease of implementation. However, the relatively empirical selection of its weight matrix can affect the control effect. This study suggests a path planning and tracking control framework for autonomous vehicles based on an upgraded RRT* and Particle Swarm Optimization Linear Quadratic Regulator (PSO-LQR) to address the abovementioned issues. Firstly, according to the driving characteristics of autonomous vehicles, a variable sampling area is used to limit the generation of random sampling points, significantly reducing the number of iterations. At the same time, an improved Artificial Potential Field (APF) method was introduced into the RRT* algorithm, which improved the convergence speed of the algorithm. Utilizing path pruning based on the maximum steering angle constraint of the vehicle and the cubic B-spline algorithm to achieve path optimization, a continuous curvature path that conforms to the precise tracking of the vehicle was obtained. In addition, optimizing the weight matrix of LQR using POS improved path-tracking accuracy. Finally, this article’s improved RRT* algorithm was simulated and compared with the RRT*, target bias RRT*, and P-RRT*. At the same time, on the Simulink–Carsim joint simulation platform, the PSO-LQR is used to track the planned path at different vehicle speeds. The results show that the improved RRT* algorithm optimizes the path search speed by 34.40% and the iteration number by 33.97%, respectively, and the generated paths are curvature continuous. The tracking accuracy of the PSO-LQR was improved by about 59% compared to LQR, and its stability was higher. The position error and heading error were controlled within 0.06 m and 0.05 rad, respectively, verifying the effectiveness and feasibility of the proposed path planning and tracking control framework.

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“…With the rapid development of artificial intelligence, autonomous vehicles (AVs) are an inevitable trend for future transportation development. They have the advantages of reducing traffic accidents, improving traffic efficiency, reducing fuel costs, and protecting the environment [ 1 , 2 ]. Under unexpected conditions such as the failure of the sensory equipment of AVs and the sudden entry of objects in front of them [ 3 ], AVs will encounter situations where collision is unavoidable, i.e., a moral dilemma.…”

Section: Introductionmentioning

confidence: 99%

A Study of Common Principles for Decision-Making in Moral Dilemmas for Autonomous Vehicles

Zhang

Wang

et al. 2022

Behavioral Sciences

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How decisions are made when autonomous vehicles (AVs) are faced with moral dilemmas is still a challenge. For this problem, this paper proposed the concept of common principles, which were drawn from the general public choice and could be generally accepted by society. This study established five moral dilemma scenarios with variables including the number of sacrifices, passenger status, presence of children, decision-making power subjects, and laws. Based on existing questionnaire data, we used gray correlation analysis to analyze the influence of the individual and psychological factors of participants in decision-making. Then, an independent sample t-test and analysis of covariance were selected to analyze the influence relationship between individual and psychological factors. Finally, by induction statistics of decision choices and related parameters of participants, we obtain common principles of autonomous vehicles, including the principle of protecting law-abiding people, the principle of protecting the majority, and the principle of protecting children. The principles have different priorities in different scenarios and can meet the complex changes in moral dilemmas. This study can alleviate the contradiction between utilitarianism and deontology, the conflict between public needs and individualized needs, and it can provide a code of conduct for ethical decision-making in future autonomous vehicles.

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Autonomous Vehicles Technological Trends

Abstract: One of the technologies widely considered to be the most promising for reducing a number of traffic-related problems, including traffic jams, safety within and outside of cities, among others, is the autonomous vehicle [...]

Cited by 7 publications

References 32 publications

Using Kolmogorov Entropy to Verify the Description Completeness of Traffic Dynamics of Highly Autonomous Driving

Using Kolmogorov Entropy to Verify the Description Completeness of Traffic Dynamics of Highly Autonomous Driving

Research on Path Planning and Tracking Control of Autonomous Vehicles Based on Improved RRT* and PSO-LQR

A Study of Common Principles for Decision-Making in Moral Dilemmas for Autonomous Vehicles

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