Search Results (112)

Autonomous driving in underground mines faces significant challenges due to Global Navigation Satellite System (GNSS) denial and harsh environmental conditions. Mainstream multi-sensor fusion and Simultaneous Localization and Mapping (SLAM) schemes have achieved substantial progress in underground navigation, but their deployment in feature-sparse tunnels may still face challenges related to computational burden and perception robustness. This study explores an infrastructure-assisted navigation architecture that transforms the roadway into a structured luminous guidance channel by deploying programmable Light Emitting Diode (LED) strips along the tunnel roof. The proposed system simplifies complex three-dimensional pose estimation into a two-dimensional visual servoing task targeting optical signals. Central to this approach is a robust data fusion strategy that utilizes a topology matching algorithm to map noisy Ultra-Wide-band (UWB) coordinates onto a discrete LED index space, thereby providing a reliable global positioning reference. Furthermore, a hierarchical fault-tolerant controller based on a Finite State Machine (FSM) is designed to facilitate seamless degradation to a UWB-assisted ultrasonic wall-following mode in the event of visual degradation, supporting fault-tolerant operation under controlled laboratory conditions. Experimental results in a laboratory simulation environment demonstrate that the system achieves millimeter-level static initialization accuracy, a dynamic tracking Root Mean Square Error of approximately 4 cm, and a 100% autonomous recovery rate from visual failures in straight tunnels. These results demonstrate the feasibility of the proposed infrastructure-assisted route under controlled laboratory conditions and suggest its potential as an engineering reference for structured underground transport scenarios with acceptable infrastructure modification. Full article

A novel procedure is proposed in this paper to investigate the capacity of parking structures to resist additional live loads that could come from many cars, potentially from heavier or driverless cars. In recent decades, the typical operating weight of passenger vehicles has risen significantly. The anticipated widespread adoption of electric vehicles (EVs), which contain heavy battery systems, may further increase live load demands. As a result, a new robust procedure is needed to assess the live load effects on parking structures. Hence, using the proposed innovative approach based on 3D influence surfaces, tributary areas (AT) and three-dimensional influence surfaces (AI) were calculated (for the first time) to examine the equivalent uniformly distributed load corresponding to selected column axial loads and beam midspan moments that are expected to be experienced during the lifetime of parking structures. As case studies, the responses of two existing multistory parking garages on the Ohio State University campus were investigated under different arrangements of two car types—standard cars and sports utility vehicles (SUVs)—and the calculated maximum live loads were compared with the current code requirements. The results show that the maximum live load for the midspan moment is conservative; however, the maximum axial column loading in the extreme scenarios presented in this paper can be larger than the specified (original) design limit of the selected parking garages. The novel methodology proposed in this paper is based on 3D influence line analysis and can be applied for any vehicle configuration and weight, and different parking arrangements or loading scenarios to investigate the performance of parking garages. Full article

The gradual integration of driverless vehicles into urban traffic systems is expected to affect both operational performance and environmental outcomes, particularly during the mixed-automation phase of urban traffic systems, in which human-driven and driverless vehicles coexist. However, existing studies have rarely examined this phase through jointly accounting for behavioral heterogeneity among human drivers and varying levels of driverless vehicle penetration in signalized urban networks. This study addresses this gap through a behavior-based microscopic traffic simulation framework that explicitly incorporates different human driving styles together with driverless vehicles across penetration levels ranging from 0% to 100%. Network- and link-level indicators, including delay, queue length, fuel consumption, and emissions, are evaluated under coordinated signal control conditions. The results reveal a nonlinear relationship between the automation level and traffic performance. While changes remain limited at low and moderate penetration levels, more pronounced improvements emerge beyond a critical threshold of approximately 75% driverless vehicle penetration. At this level, network-wide average delay reductions of about 3–5% are observed, accompanied by consistent decreases in fuel consumption and emissions. By highlighting how behavioral interactions shape the effectiveness of automation, the findings provide practical insights for traffic engineers and urban planners, supporting the design and evaluation of signalized urban arterials under mixed traffic conditions while contributing to environmental sustainability and sustainable urban mobility through improved traffic efficiency and stability. Full article

This study presents the development and implementation of an electronically actuated steering system in a Formula Student Driverless race car, aiming to support autonomous driving capability. A DC motor with a belt-drive mechanism was integrated into the original steering rack assembly without altering its core mechanical characteristics. The research also includes a validation of the steering geometry using both physical measurements and CAD simulations. The objective of this measurement is to determine the steering angle as a function of the steering wheel input angle, ensuring that the resulting data accurately informs vehicle dynamics models such as the kinematic bicycle model. These steps form the basis for closed-loop control integration in the autonomous driving platform. Full article

The optimal configuration of perception systems in autonomous vehicles is essential for accurate environmental sensing, precise navigation, and overall operational safety. In Formula Student Driverless (FSD) vehicles, sensor placement is particularly challenging due to the compact design constraints and the highly dynamic nature of the racing environment. This study investigates the positioning and configuration of two LIDAR sensors and a stereo camera on an FSD race car, focusing on field-of-view coverage, sensing redundancy, and sensor fusion potential. To achieve a comprehensive evaluation, measurements are conducted exclusively in a simulation environment, where field-of-view maps are generated, detection ranges are analyzed, and perception reliability is assessed under various conditions. The results provide insights into the optimal sensor arrangement that minimizes blind spots, maximizes sensing accuracy, and enhances the efficiency of the autonomous vehicle’s perception architecture. Full article

In complex scenarios, such as industrial parks and underground parking lots, efficient and safe autonomous navigation is essential for driverless operation and automatic parking. However, conventional modular navigation methods, especially the A* algorithm, suffer from excessive node traversal and short paths that bring vehicles dangerously close to obstacles. To address these issues, we propose an autonomous navigation approach based on a layered terrain cost map and a nonlinear predictive control model, which ensures real-time performance, safety, and reduced computational cost. The global planner applies a two-stage A* strategy guided by the hierarchical terrain cost map, improving efficiency and obstacle avoidance, while the local planner combines linear interpolation with nonlinear model predictive control to adaptively adjust the vehicle speed under varying terrain conditions. Experiments conducted in simulated and real underground parking scenarios demonstrate that the proposed method significantly improves the computational efficiency and navigation safety, outperforming the traditional A* algorithm and other baseline approaches in overall performance. Full article

The prioritization of user requirements is neglected in most existing interface designs for driverless vehicle systems, which may incur safety risks, fragmented user experiences, development resource wastage, and weakened market competitiveness. Accordingly, this paper proposes a hybrid interface design method for a virtual reality (VR) driverless vehicle system by combining a A-KANO model and system usability scale (SUS). Firstly, we obtain key words, and a total of 23 demand points are collected through word frequency analysis via combining with user interview and observation method; secondly, 21 demand points are derived from A-KANO model analysis and prioritized for function development; and finally, design practice is carried out according to the ranking results, and virtual reality technology is used to build a VR unmanned vehicle system in order to simulate the interface interaction of a driverless vehicle system. Then, the VR driverless vehicle system is used as a test experimental environment for user evaluation, and combined with the SUS scale to evaluate the user-prioritized experience requirements for practical verification. Empirical results demonstrate that this method effectively categorizes multifaceted user needs, providing actionable solutions to enhance passenger experience and optimize service system design in future autonomous driving scenarios. Full article

Vehicle detection plays a pivotal role in traffic management as a key technology for intelligent traffic management and driverless driving. However, current deep learning-based vehicle detection models face several challenges in practical applications. These include slow detection speeds, large computational and parametric quantities, high leakage and misdetection rates in target-intensive environments, and difficulties in deploying them on edge devices with limited computing power and memory. To address these issues, this paper proposes an improved vehicle detection method called SMA-YOLO, based on the YOLOv7 model. Firstly, MobileNetV3 is adopted as the new backbone network to lighten the model. Secondly, the SimAM attention mechanism is incorporated to suppress background interference and enhance small-target detection capability. Additionally, the ACON activation function is substituted for the original SiLU activation function in the YOLOv7 model to improve detection accuracy. Lastly, SIoU is used to replace CIoU to optimize the loss of function and accelerate model convergence. Experiments on the UA-DETRAC dataset demonstrate that the proposed SMA-YOLO model achieves a lightweight effect, significantly reducing model size, computational requirements, and the number of parameters. It not only greatly improves detection speed but also maintains higher detection accuracy. This provides a feasible solution for deploying a vehicle detection model on embedded devices for real-time detection. Full article

Driverless electric formula racing cars are affected by nonlinear vehicle characteristics, perturbations, and parameter uncertainties during races, which can cause problems such as low accuracy and instability in trajectory tracking. Aiming to address such problems, this paper proposes a control method combining a prescribed performance control with adaptive backstepping fuzzy control (PPC-ABFC) to solve the aforementioned issues and improve the trajectory tracking accuracy and stability of racing cars. This control method is achieved by constructing a combined error model and confining the error within a prescribed performance function. The nonlinear terms, disturbances, and unknown parameters of the model are approximated by a fuzzy logic system (FLS). An adaptive parameter update law is designed to update the learning parameters in real time. The virtual control law and the real control law were designed by using the backstepping method. The stability of the PPC-ABFC closed-loop system was rigorously proved by applying the Lyapunov stability theory. Finally, simulations were conducted to compare the proposed PPC-ABFC method with other algorithms at different speeds. The results demonstrated that the PPC-ABFC method effectively enhances the trajectory tracking performance of driverless electric formula racing cars. Full article

The rapid development of autonomous driving technology has been a key driver for the emergence of driverless delivery vehicles. To promote wider adoption, it is essential to address consumers’ concerns about safety and reliability, leverage psychological factors, and implement supportive policies that encourage technology adoption while ensuring public safety and privacy. Therefore, it is necessary to explain and predict consumers’ behavior and intention to adopt driverless delivery vehicles. To this end, this study extends the Technology Acceptance Model (TAM) to include technological complexity and perceived trust. This study evaluates the model by applying necessary condition analysis (NCA) and partial least squares structural equation modeling (PLS-SEM) to analyze data from 579 respondents from Jiangsu Province, China. This study explores the sustainability implications of autonomous delivery vehicles, highlighting their potential to reduce environmental impact and promote a more sustainable transportation system. The outcomes indicate that perceived ease of use (PEU), attitude, perceived trust, technological complexity (TECOM), and perceived usefulness (PU) are significant determinants and necessary conditions of consumers’ intention to adopt driverless delivery vehicles. Perceived trust and TECOM had a significant and indirect influence on consumers’ intention to adopt driverless delivery vehicles via PU and PEU. Perceived trust and technological complexity had a substantial impact on consumers’ adoption intention of driverless delivery vehicles. The study recommends that managers work closely with regulators to ensure their technologies meet all local standards and regulations. It also recommends its potential to reduce carbon emissions, improve energy efficiency, and contribute to a more sustainable transportation system. Full article

Over the last decade, a transformation in the automotive industry has been witnessed, as advancements in artificial intelligence and sensor technology have continued to accelerate the development of driverless vehicles. These systems are expected to significantly reduce traffic accidents and associated costs, making their integration into future transportation systems highly impactful. To explore this field in a controlled and flexible manner, scaled autonomous vehicle platforms are increasingly adopted for experimentation. In this work, we propose a set of methodologies to perform autonomous driving tasks through a software–hardware co-design approach. The developed system focuses on deploying a modular and reconfigurable software stack tailored to run efficiently on constrained embedded hardware, demonstrating a balance between real-time capability and computational resource usage. The proposed platform was implemented on a 1:10 scale vehicle that participated in the Bosch Future Mobility Challenge (BFMC) 2024. It integrates a high-performance embedded computing unit and a heterogeneous sensor suite to achieve reliable perception, decision-making, and control. The architecture is structured across four interconnected layers—Input, Perception, Control, and Output—allowing flexible module integration and reusability. The effectiveness of the system was validated throughout the competition scenarios, leading the team to secure first place. Although the platform was evaluated on a scaled vehicle, its underlying software–hardware principles are broadly applicable and scalable to larger autonomous systems. Full article

The automotive industry is fully shifting towards autonomous connected vehicles. By advancing vehicles’ intelligence and connectivity, the industry has enabled innovative functions such as advanced driver assistance systems (ADAS) in the direction of driverless cars. Such functions are often referred to as cyber-physical features, since almost all of them require collecting data from the physical environment to make automotive operation decisions and properly actuate in the physical world. However, increased functionalities result in increased complexity, which causes serious security vulnerabilities that are typically a result of mushrooming functionality and hence complexity. In a world where we keep seeing traditional mechanical systems shifting to x-by-wire solutions, the number of connected sensors, processing systems, and communication buses inside the car exponentially increases, raising several safety and security concerns. Because there is no safety without security, car manufacturers start struggling in making lightweight sensor and processing systems while keeping the security aspects a major priority. This article surveys the current technological challenges in securing autonomous vehicles and contributes a cross-layer analysis bridging hardware security primitives, real-world side-channel threats, and redundancy-based fault tolerance in automotive electronic control units (ECUs). It combines architectural insights with an evaluation of commercial support for TrustZone, trusted platform modules (TPMs), and lockstep platforms, offering both academic and industry audiences a grounded perspective on gaps in current hardware capabilities. Finally, it outlines future directions and presents a forward-looking vision for securing sensors and processing systems in the path toward fully safe and connected autonomous vehicles. Full article

The behavior decision control of autonomous vehicles is a critical aspect of advancing autonomous driving technology. However, current behavior decision algorithms based on deep reinforcement learning still face several challenges, such as insufficient safety and sparse reward mechanisms. To solve these problems, this paper proposes a dueling double deep Q-network based on dual-priority experience replay—DPD3QN. Initially, the dueling network is integrated with the double deep Q-network, and the original network’s output layer is restructured to enhance the precision of action value estimation. Subsequently, dual-priority experience replay is incorporated to facilitate the model’s ability to swiftly recognize and leverage critical experiences. Ultimately, the training and evaluation are conducted on the OpenAI Gym simulation platform. The test results show that DPD3QN helps to improve the convergence speed of driverless vehicle behavior decision-making. Compared with the currently popular DQN and DDQN algorithms, this algorithm achieves higher success rates in challenging scenarios. Test scenario I increases by 11.8 and 25.8 percentage points, respectively, while the success rates in test scenarios I and II rise by 8.8 and 22.2 percentage points, respectively, indicating a more secure and efficient autonomous driving decision-making capability. Full article

Autonomous electric vehicle (AEV) services leverage advanced autonomous driving and electric vehicle technologies to provide innovative, driverless transportation solutions. The biggest challenge faced by AEVs is the limited number of charging stations and long charging times. A critical challenge is maximizing passenger travel satisfaction while reducing the AEV idle time. This involves coordinating passenger transport and charging tasks via leveraging the information from charging stations, passenger transport, and AEV data. There are four important contributions in this paper. Firstly, we introduce an integrated scheduling model that considers both passenger transport and charging tasks. Secondly, we propose a multi-level differentiated charging threshold strategy, which dynamically adjusts the charging threshold based on both AEV battery levels and the availability of charging stations, reducing competition among vehicles and minimizing waiting times. Thirdly, we develop a rapid strategy to optimize the selection of charging stations by combining geographic and deviation distance. Fourthly, we design a new evolutionary algorithm to solve the proposed model, in which a buffer space is introduced to promote diversity within the population. Finally, experimental results show that compared to the existing state-of-the-art scheduling algorithms, the proposed algorithm shortens the running time of scheduling algorithms by 6.72% and reduces the idle driving time of AEVs by 6.53%, which proves the effectiveness and efficiency of the proposed model and algorithm. Full article

Within the area of environmental perception, automatic navigation, object detection, and computer vision are crucial and demanding fields with many applications in modern industries, such as multi-target long-term visual tracking in automated production, defect detection, and driverless robotic vehicles. The performance of computer vision has greatly improved recently thanks to developments in deep learning algorithms and hardware computing capabilities, which have spawned the creation of a large number of related applications. At the same time, with the rapid increase in autonomous systems in the market, energy consumption has become an increasingly critical issue in computer vision and SLAM (Simultaneous Localization and Mapping) algorithms. This paper presents the results of a detailed review of over 100 papers published over the course of two decades (1999–2024), with a primary focus on the technical advancement in computer vision. To elucidate the foundational principles, an examination of typical visual algorithms based on traditional correlation filtering was initially conducted. Subsequently, a comprehensive overview of the state-of-the-art advancements in deep learning-based computer vision techniques was compiled. Furthermore, a comparative analysis of conventional and novel algorithms was undertaken to discuss the future trends and directions of computer vision. Lastly, the feasibility of employing visual SLAM algorithms in the context of autonomous vehicles was explored. Additionally, in the context of intelligent robots for low-carbon, unmanned factories, we discussed model optimization techniques such as pruning and quantization, highlighting their importance in enhancing energy efficiency. We conducted a comprehensive comparison of the performance and energy consumption of various computer vision algorithms, with a detailed exploration of how to balance these factors and a discussion of potential future development trends. Full article