Search Results (59)

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

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 driving technologies for environmental perception are mostly based on visual cues obtained from sensors like cameras, RADAR, or LiDAR. They capture the environment as if seen through “human eyes”. If this visual information is complemented with auditory information, thereby also providing “ears”, driverless cars can become more reliable and safer. In this paper, an Acoustic Event Detection model is presented that can detect various acoustic events in an automotive context along with their time of occurrence to create an audio scene description. The proposed detection methodology uses the pre-trained network Bidirectional Encoder representation from Audio Transformers (BEATs) and a single-layer neural network trained on the database of real audio recordings collected from different cars. The performance of the model is evaluated for different parameters and datasets. The segment-based results for a duration of 1 s show that the model performs well for 11 sound classes with a mean accuracy of 0.93 and F1-Score of 0.39 for a confidence threshold of 0.5. The threshold-independent metric mAP has a value of 0.77. The model also performs well for sound mixtures containing two overlapping events with mean accuracy, F1-Score, and mAP equal to 0.89, 0.42, and 0.658, respectively. Full article

With the application and promotion of space geodesy, the popularization of remote sensing technology, and the development of artificial intelligence, a more accurate and stable Terrestrial Reference Frame (TRF) has become more urgent. For example, sea level change detection, crustal deformation monitoring, and driverless cars, among others, require the accuracy of the terrestrial reference frame to be better than 1 mm in positioning and 0.1 mm/a in velocity, respectively. However, the current frequently used ITRF2014 and ITRF2020 do not satisfy such requirements. Therefore, this paper analyzes the coordinate residual time series data of linear TRFs and finds there are still some unlabeled jumps and time-dependent periodic signals, especially in the GNSS coordinate residuals, which can lead to incorrect station epoch coordinates and velocities, further affecting the accuracy and stability of the TRF. The unlabeled jumps could be detected by the sequential t-test analysis of regime shifts (STARS) combined with the generalized extreme Studentized deviate (GESD) algorithms introduced in our earlier paper. These nonlinear time-dependent periodic signals could be modeled better by singular spectrum analysis (SSA) with respect to least squares fitting; the fitting period is no longer composed of semi-annual and annual items, as with ITRF2014. The periods of continuous coordinate residual time series data longer than 5 years are obtained by FFT. The results show that there are no period signals for individual SLR/VLBI sites, and there are still other period terms, such as 34 weeks, 20.8 weeks and 17.3 weeks, in addition to semi-annual and annual items for some GNSS sites. Moreover, after SSA corrections, the re-calculated TRF and the corresponding EOP could be obtained, based on data from the Chinese Earth Rotation and Reference System Service (CERS) TRF and the Earth Orientation Parameter (EOPs) multi-technique determination software package (CERS TRF&EOP V2.0) developed by the Shanghai Astronomical Observatory (SHAO). Their accuracy could be evaluated with respect to the ITRF2014 and the IERS 14 C04, respectively. The results show that the accuracy and stability of the newly established a nonlinear TRF and EOP based on SSA have been greatly improved and better than a linear TRF and EOP. SSA is better than least squares fitting, especially for those coordinate residual time series with varying amplitude and phase. For GPS, comparing with the ITRF2014, the station coordinate accuracy of 10.8% is better than 1 mm, and the station velocity accuracy of 4.4% is better than 0.1 mm/year. There are 3.1% VLBI stations, for which coordinate accuracy is better than 1 mm and velocity accuracy is better than 0.1 mm/year. However, there are no stations with coordinates and velocities better than 1 mm and 0.1 mm/year for the SLR and DORIS. The WRMS values of polar motion x, polar motion y, LOD, and UT1-UTC are reduced by 2.4%, 3.2%, 2.7%, and 0.96%, respectively. The EOP’s accuracy in SOL-B, in addition to LOD, is better than that of the JPL. 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

Promoting the deployment and application of embedded systems in complex forest scenarios is an inevitable developmental trend in advanced intelligent forestry equipment. Unstructured roads, which lack effective artificial traffic signs and reference objects, pose significant challenges for driverless technology in forest scenarios, owing to their high nonlinearity and uncertainty. In this research, an unstructured road parameterization construction method, “DeepLab-Road”, based on tight coupling of multisource information is proposed, which aims to provide a new segmented architecture scheme for the embedded deployment of a forestry engineering vehicle driving assistance system. DeepLab-Road utilizes MobileNetV2 as the backbone network that improves the completeness of feature extraction through the inverse residual strategy. Then, it integrates pluggable modules including DenseASPP and strip-pooling mechanisms. They can connect the dilated convolutions in a denser manner to improve feature resolution without significantly increasing the model size. The boundary pixel tensor expansion is then completed through a cascade of two-dimensional Lidar point cloud information. Combined with the coordinate transformation, a quasi-structured road parameterization model in the vehicle coordinate system is established. The strategy is trained on a self-built Unstructured Road Scene Dataset and transplanted into our intelligent experimental platform to verify its effectiveness. Experimental results show that the system can meet real-time data processing requirements (≥12 frames/s) under low-speed conditions (≤1.5 m/s). For the trackable road centerline, the average matching error between the image and the Lidar was 0.11 m. This study offers valuable technical support for the rejection of satellite signals and autonomous navigation in unstructured environments devoid of high-precision maps, such as forest product transportation, agricultural and forestry management, autonomous inspection and spraying, nursery stock harvesting, skidding, and transportation. Full article

As we move into the next stages of the technological revolution, artificial intelligence (AI) that is explainable and sustainable is becoming a key goal for researchers across multiple domains. Leveraging the concept of functional connectivity (FC) in the human brain, this paper provides novel research directions for neuromorphic machine intelligence (NMI) systems that are energy-efficient and human-compatible. This review serves as an accessible review for multidisciplinary researchers introducing a range of concepts inspired by neuroscience and analogous machine learning research. These include possibilities to facilitate network integration and segregation in artificial architectures, a novel learning representation framework inspired by two FC networks utilised in human learning, and we explore the functional connectivity underlying task prioritisation in humans and propose a framework for neuromorphic machines to improve their task-prioritisation and decision-making capabilities. Finally, we provide directions for key application domains such as autonomous driverless vehicles, swarm intelligence, and human augmentation, to name a few. Guided by how regional brain networks interact to facilitate cognition and behaviour such as the ones discussed in this review, we move toward a blueprint for creating NMI that mirrors these processes. Full article

Environment perception technology is the most important part of driverless technology, and driverless vehicles need to realize decision planning and control by virtue of perception feedback. This paper summarizes the most promising technology methods in the field of perception, namely visual perception technology, radar perception technology, state perception technology, and information fusion technology. Regarding the current development status in the field, the development of the main perception technology is mainly the innovation of information fusion technology and the optimization of algorithms. Multimodal perception and deep learning are becoming popular. The future of the field can be transformed by intelligent sensors, promote edge computing and cloud collaboration, improve system data processing capacity, and reduce the burden of data transmission. Regarding driverless vehicles as a future development trend, the corresponding technology will become a research hotspot. Full article

Currently, we exist in the 5G division of the wireless technology cycle, where the standardization is complete and deployment is being carried out. However, 5G networks do not have the capacity to deliver an automated and intelligent network that supports connected intelligence. 6G is what enables this, and globally, countries are aiming to lay the foundation for the communication needs of 2030. This brings out a very key question and discussion on how wireless communications will develop in the future, particularly adapting to the range and set of applications and user cases. Industry and academic efforts have started to explore beyond 5G and uncover 6G as 5G becomes more internationally accessible. We forecast that 6G will undergo a transition that is unheard of in the history of wireless cellular systems. 6G exists beyond mobile internet and will be required to support omnipresent AI services from the network’s core to its endpoints. Meanwhile, artificial intelligence (AI) will be crucial for developing and improving 6G designs, protocols, and operations. URLLC plays a crucial role in next-generation communication systems, particularly in 6G, for applications requiring ultra-low latency and reliability. These services support cutting-edge technologies like driverless vehicles, remote robotic surgery, smart factories, and augmented reality applications. URLLCs ensure robust connectivity and real-time responsiveness, enabling time-sensitive and safety-critical services in 6G communication infrastructures. This article illustrates the importance of URLLCs in 6G and their integration with deep learning, the security challenges, and their potential solutions. Further on, it establishes its relationship with key aspects of federated learning and security in the 6G domain. Full article

As technology advances in almost all areas of life, many companies and researchers are working to develop fully autonomous vehicles. Such level 5 autonomous driving, unlike levels 0 to 4, is a driverless vehicle stage and so the leap from level 4 to level 5 autonomous driving requires much more research and experimentation. For autonomous vehicles to safely drive in complex environments, autonomous cars should ensure end-to-end delay deadlines of sensor systems and car-controlling algorithms including machine learning modules, which are known to be very computationally intensive. To address this issue, we propose a new framework, i.e., an environment-driven approach for autonomous cars. Specifically, we identify environmental factors that we cannot control at all, and controllable internal factors such as sensing frequency, image resolution, prediction rate, car speed, and so on. Then, we design an admission control module that allows us to control internal factors such as image resolution and detection period to determine whether given parameters are acceptable or not for supporting end-to-end deadlines in the current environmental scenario while maintaining the accuracy of autonomous driving. The proposed framework has been verified with an RC car and a simulator. Full article

Agricultural practices such as tilling, sowing, cropping, It’s duty but arvesting, and land-use patterns in any agrarian economy depend on climate. Therefore, any adverse climatic conditions can seriously affect the production or yield of crops. Increased temperature enhances the susceptibility of crops to pests and various plant diseases. Weeds are also known to multiply rapidly and decrease the nutritive value of soil, negatively affecting crop production. Our present study is designed to address similar problems faced by the farming community in the South-24 Parganas district of West Bengal, India, and suggest several probable technological solutions. Importantly, West Bengal is included in one of the six agro-climatic zones. Major crops from this study site are rice, wheat, maize, jute, green gram, black gram, pigeon pea, lentils, sugarcane, pulses, rapeseed, mustard, sesame, linseed, and vegetables. Significantly, cultivable land area has decreased in comparison to the overall crop area in this region. Reduced interest in agriculture, irrigation problems, increased profit in the non-agricultural economy, and rapid conversion of agricultural land for commercial purposes (construction of plots, hatcheries for fishing practices), along with uncertainties associated with rainfall patterns and frequent cyclones, are matters of grave concern in this study area. Agricultural scientists, researchers, environmentalists, local bodies, and government organizations are suggesting alternatives to benefit farmers. Thus, precision agriculture or crop management is required to recognize site-specific variables within agricultural lands and formulate strategies for improving decision-making regarding crop sowing, appropriate use of herbicides, weedicides, and precision irrigation, along with innovative harvesting technologies. Thus, the present paper provides a vision for the farming community in our study area to overcome their traditional practices and adopt different techniques of precision agriculture to increase flexibility, performance, accuracy, and cost-effectiveness. Soil temperature, humidity, and moisture monitoring sensors could be beneficial. Precision soil management, precision irrigation, crop disease management, weed management, and harvesting technologies are the different modules considered for discussion in this paper. Machine learning algorithms, such as decision tree, K-nearest neighbor (KNN), Gaussian naïve Bayes (GNB), K-means clustering, artificial neural network (ANN), fuzzy logic system (FLS), and support vector machine (SVM), could prove helpful for progressive farmers. The use of AI-powered weeding machines, drones, and UAVs for rapid weed removal and the localized application of herbicides and pesticides could also improve the accuracy and efficiency of agriculture. Utilizing drones fitted with high-resolution cameras could help gather precision field images, proving to be quite helpful in crop monitoring and crop health assessment. Unmanned driverless tractors and harvesting machines using robotics integrated with data from GPS/GIS sensors or radars could also be considered an effective and time-saving option. Thus, machine learning, along with innovative agricultural technologies, could contribute to improving the livelihoods of the farming fraternity. Full article