Driverless Cars Research Articles

Wifi 7: Advancements, Applications and Future Prospects

The goal of Wi-Fi 7, a major advancement in wireless networking technology, is to satisfy the increasing demand for faster data speeds, reduced latency, and more spectrum economy. Building on the success of Wi-Fi 6 and 6E. this next generation offers previously unheard-of speeds of up to 30 Gbps and improved network stability with innovations like 320 MHz channel width, 4096-QAM (Quadrature Amplitude Modulation), and Multi-Link Operation (MLO). Virtual/augmented reality (VR/AR). high-definition media streaming, sophisticated loT ecosystems, and industrial automation arc just a few of the many uses that Wi-Fi 7 is anticipated to enable. Wi-Fi 7 is expected to serve new use cases that require dependable, fast wireless connections, such smart cities, real-time telemedicine, and driverless cars, thanks to its extremely low latency and smooth multi-connection management

Research on image recognition and processing application technology of unmanned vehicle based on deep learning

Abstract. Image recognition technology is critically important in various fields, including the rapidly advancing sector of autonomous vehicles. As one of the core components enabling driverless cars to perceive their environment and make informed decisions, image recognition has seen significant advancements due to deep learning. This paper focuses on the application of deep learning in image recognition for self-driving cars and explores its implications for the future of autonomous driving technology. To begin with, this paper examines the empirical evaluation of deep learning models in highway driving scenarios. By employing Convolutional Neural Networks (CNN), these models achieve high detection rates and superior accuracy in recognizing vehicles and lanes. The robustness of these models is tested under varying weather conditions and times, demonstrating their effectiveness compared to classical computer vision techniques. Next, the paper discusses radar-camera fusion technology, highlighting different fusion strategies such as data-level, feature-level, object-level, and hybrid-level. The findings suggest that while feature-level fusion excels in detecting small objects in complex scenes, hybrid-level fusion is optimal for diverse driving situations. This section provides valuable insights into the integration of multimodal data for improved object recognition and semantic segmentation. After discussing fusion technologies, the paper finally reviews the security challenges posed by adversarial attacks on deep learning-based unmanned systems.

Comprehensive analysis of services towards Data Aggregation, Data Fusion and enhancing security in IoT-based smart home

Data aggregation and sensors data fusion would be very helpful in a number of developing fields, including deep learning, driverless cars, smart cities, and the Internet of Things (IoT). An advanced smart home application will test the upgraded Constrained Application Protocol (CoAP) using Contiki Cooja. Smart home can enhance people’s comfort. Secure authentication between the transmitter and recipient nodes is essential for providing IoT services. In many IoT applications, device data are critical. Current encryption techniques use complicated arithmetic for security. However, these arithmetic techniques waste power. Hash algorithms can authenticate these IoT applications. Mobile protection issues must be treated seriously, because smart systems are automatically regulated. CoAP lets sensors send and receive server data with an energy-efficient hash function to increase security and speed. SHA224, SHA-1, and SHA256 were tested by the CoAP protocol. Proposed model showed that SHA 224 starts secure sessions faster than SHA-256 and SHA-1. The ChaCha ci. This study proposed enhanced ChaCha, a stream cipher for low-duty-cycle IoT devices. For wireless connections between the IoT gateway and sensors with a maximum throughput of 1.5 Mbps, the proposed model employs a wireless error rate (WER) of 0.05; the throughput rises with an increase in the transmission data rate.

RS-SLAM: real time semantic slam with driverless car using LiDAR-camera-IMU sensing

Abstract Accurate and robust Simultaneous Localization and Mapping (SLAM) technology is a critical component of driverless cars, and semantic information plays a vital role in their analysis and understanding of the scene. In the actual scene, the moving object will produce the shadow phenomenon in the mapping process. The positioning accuracy and mapping effect will be affected. Therefore, we propose a semantic SLAM framework combining LiDAR, IMU, and camera, which includes a semantic fusion front-end odometry module and a closed-loop back-end optimization module based on semantic information. An improved image semantic segmentation algorithm based on Deeplabv3+ is designed to enhance the performance of the image semantic segmentation model by replacing the backbone network and introducing an attention mechanism to ensure the accuracy of point cloud segmentation. Dynamic objects are detected and eliminated by calculating the similarity score of semantic labels. A loop closure detection method based on semantic information is proposed to detect key semantic features and use threshold range detection and point cloud re-matching to establish the correct loop closure detection, and finally reduce the global cumulative error and improve the global trajectory accuracy using graph optimization to ultimately obtain the global motion trajectory and realize the construction of 3D semantic maps. We evaluated it on the KITTI dataset and collected a dataset for evaluation by ourselves, which includes four different sequences. The results show that the proposed framework has good positioning accuracy and mapping effect in large-scale urban road environments.

Navigating the Autonomous Era: A Detailed Survey of Driverless Cars

Navigating the Autonomous Era: A Detailed Survey of Driverless Cars

Deep learning in driverless: Research results, issues, challenges

As computing and communication technologies continue to evolve, leading technology companies are investing significant resources in self-driving car techniques. With the use of deep learning in driverless driving, driverless cars are enabling them to sense, make decisions and control in complex environments, leading to truly autonomous driving. Deep learning is used for environment perception, which enables driverless cars to understand their surroundings by recognizing information such as road surfaces, pedestrians and vehicles, and also used for decision formulation, by predicting the behavior of other vehicles, pedestrians and identifying the environment around the road conditions to control the driverless car to make the optimal driving decision. But in order to achieve fully autonomous driving, there are still some technical aspects. Therefore, we deep learning in the existing driverless technology is reviewed, mainly discussing the application in the direction of driverless implementation and safety inspection. Through reading a large amount of literature, we discuss the problems that hinder the development of the driverless field, and at the same time, how to solve the problems in the future to promote the breakthrough of the driverless field. The application of deep learning in driverless not only enhances the intelligence level of driverless cars, but also prepares for the commercial application of driverless cars in the future. It enables driverless cars to better identify and adapt to complex traffic environments. Finally, realizing truly autonomous driving.

Study on enhanced real time applications of compound semiconductor (SiC and GaN) power devices with AI and IoT Technologies

Compound semiconductors, composed of two or more elements, differ from single-element semiconductors like silicon. These materials are crucial because they have a direct band gap, unlike elemental semiconductors such as silicon and germanium, making them ideal for optoelectronic applications like LEDs, semiconductor lasers, and photo detectors. Robots rely on sophisticated sensors to collect vital data for their operation, including internal data on temperature, moisture, movement, and position, as well as external data from images, infrared light, and sound, processed through semiconductor units. Compound semiconductors are integral to numerous technologies around us, including electric cars, solar panels, satellites, spacecraft, and smart phones. Future innovations like driverless cars and artificial intelligence will also heavily depend on these materials.

Control Systems Design for Autonomous Vehicles: Mathematical Approaches to Path Planning and Trajectory Tracking

Self-driving cars are a big change in the way people get around. They promise to make transportation safer, more efficient, and more convenient. Strong control system design, which includes path planning and trajectory tracking, is a key part of how they work. This essay looks at mathematics methods that are important for reaching these goals. The path planning problem is to find a way to get from where you start to where you want to go while taking into account natural and changing limits. Different methods, like geometric algorithms, optimization techniques, and statistical approaches, are used to find lines that don't collide and maximize things like time, energy, and comfort. Trajectory tracking, on the other hand, is the process of sticking to the planned path even when there are problems or unknowns. Model predictive control, feedback linearization, and adaptive control are some of the control methods that are used to make sure the car stays stable and follows the path that is wanted. One of the hardest parts of controlling a driverless car is balancing the different goals of planning a path and following its current path in real time. Because of this, we need to create combined control systems that can smoothly handle the planning and performance steps while taking into account how the surroundings and car state change over time. Also, the rise of linked and self-driving cars opens the door to joint control methods, in which cars talk to each other and work together to improve safety and traffic flow. Vehicle-to-vehicle contact is used by cooperative route planning algorithms to coordinate moves and avoid crashes, which makes the system more reliable and efficient as a whole.

On-Device Neural Network for Object Train and Recognition using Mobile

A neural network is a machine learning (ML) program or model that processes information and recognizes patterns, similar to the human brain. The neural network algorithm operates by training on data to learn and enhance its accuracy. If there is any mistake in the learning process, the machine will react unexpectedly and produce incorrect information. So, whenever a neural network model is developed, it is mandatory to evaluate its performance and ensure its output is accurate. Cameras, touchscreens, internet connectivity, and powerful CPUs have contributed to the popularity of smartphones. Mobile apps are software applications that run on smartphones. ML enhances mobile app functionality by offering features such as voice recognition, image analysis, natural language processing, personalization, and recommendations. Training ML models using mobile apps is challenging due to limited resources, data, and privacy concerns. Object recognition is a neural network-based technique that enables the identification and localization of objects in an image or video. This technique is used in driverless cars, disease identification, industrial inspection, robotics, and more. In this paper, the author introduces a new neural network-based algorithm that utilizes mobile devices for training and recognizing images. Although mobile devices have some technological limitations for training, well-established guidelines for systematically mapping verification and validation techniques have been used in the proposed neural network to ensure performance and correctness in on-device object training and recognition.

A governance perspective on user acceptance of autonomous systems in Singapore

Autonomous systems that operate without human intervention by utilising artificial intelligence are a significant feature of the fourth industrial revolution. Various autonomous systems, such as driverless cars, unmanned drones and robots, are being tested in ongoing trials and have even been adopted in some countries. While there has been a discussion of the benefits and risks of specific autonomous systems, more needs to be known about user acceptance of these systems. The reactions of the public, especially regarding novel technologies, can help policymakers better understand people's perspectives and needs, and involve them in decision-making for governance and regulation of autonomous systems. This study has examined the factors that influence the acceptance of autonomous systems by the public in Singapore, which is a forerunner in the adoption of autonomous systems. The Unified Technology Adoption and Use Theory (UTAUT) is modified by introducing the role of government and perceived risk in using the systems. Using structural equation modelling to analyse data from an online survey (n = 500) in Singapore, we find that performance expectancy, effort expectancy, social influence, and trust in government to govern autonomous systems significantly and positively impact the behavioural intention to use autonomous systems. Perceived risk has a negative relationship with user acceptance of autonomous systems. This study contributes to the literature by identifying latent variables that affect behavioural intention to use autonomous systems, especially by introducing the factor of trust in government to manage risks from the use of these systems and filling the gap by studying the entire domain of autonomous systems instead of a narrow focus on one application. The findings will enable policymakers to understand the perceptions of the public in regard to adoption and regulation, and designers and manufacturers to improve user experience.

Smart Urban Food Re-Distribution using Web Application

Smart Urban Food Re-Distribution using Web Application

Will automated vehicles encourage more jaywalking? Results from a stated preference survey

When interacting with driverless cars, pedestrians might be emboldened to jaywalk knowing the vehicles will slow down for them automatically. Increased jaywalking might disrupt traffic flow and pose challenges for traffic law enforcement. Few studies examined pedestrians’ attitudes toward driverless cars’ safety implication, management strategies for the interactions between pedestrians and driverless cars, and how pedestrians would expect their street crossing behavior to change in the presence of driverless cars. We conducted an online stated preference survey of 1,000 residents in the Philadelphia and the Seattle metropolitan areas to investigate respondents’ attitudes toward driverless cars and their preferences of jaywalking under different driverless car saturation levels. Through descriptive data analysis and multilevel binomial logit analysis, we found that overall pedestrians felt less safe with driverless cars on the road. Pedestrians preferred limiting the speeds of driverless cars or disabling driverless functions in areas with heavy pedestrian traffic to increasing enforcement for jaywalking laws. Multilevel binomial logit analysis showed pedestrians were more likely to jaywalk in the presence of driverless cars in high visibility and low traffic volume, and were deterred by bigger, faster streets. Respondents’ current crossing behavior might serve as an indicator of their crossing behavior in the presence of driverless cars. Our findings remind cities that sound street design strategies could help to ensure safer interactions between pedestrians and driverless cars and greater acceptance of driverless cars on urban streets.

Pedestrian Perception Tracking in Complex Environment of Unmanned Vehicles Based on Deep Neural Networks

INTRODUCTION: In recent years, machine learning and deep learning have emerged as pivotal technologies with transformative potential across various industries. Among these, the automobile industry stands out as a significant arena for the application of these technologies, particularly in the development of smart cars with unmanned driving systems. This article delves into the extensive research conducted on the detection technology employed by autonomous vehicles to navigate road conditions, a critical aspect of driverless car technology.
 OBJECTIVES: The primary aim of this research is to explore and highlight the intricacies of road condition detection for autonomous vehicles. Emphasizing the importance of this key component in the development of driverless cars, we aim to provide insights into cutting-edge algorithms that enhance the capabilities of these vehicles, ultimately contributing to their widespread adoption.
 METHODS: In addressing the challenge of road condition detection, we introduce the TidyYOLOv4 algorithm. This algorithm, deemed more advantageous than YOLOv4, particularly excels in pedestrian recognition within urban traffic environments. Its real-time capabilities make it a suitable choice for detecting pedestrians on the road under dynamic conditions.
 RESULTS: The application of the TidyYOLOv4 algorithm in autonomous vehicles has yielded promising results, especially in enhancing pedestrian recognition in urban traffic settings. The algorithm's real-time functionality proves crucial in ensuring the timely detection of pedestrians on the road, thereby improving the overall safety and efficiency of autonomous vehicles.
 CONCLUSION: In conclusion, the detection of road conditions is a critical aspect of autonomous vehicle technology, with implications for safety and efficiency. The TidyYOLOv4 algorithm emerges as a noteworthy advancement, outperforming its predecessor YOLOv4 in pedestrian recognition within urban traffic environments. As companies continue to invest in driverless technology, leveraging such advanced algorithms becomes imperative for the successful deployment of autonomous vehicles in real-world scenarios.

A Moving Metaverse: QoE challenges and standards requirements for immersive media consumption in autonomous vehicles

The evolution of Autonomous Vehicles (AVs) has blurred the distinction between drivers and passengers, resulting in increased demand for in-car entertainment. Simultaneously, social networking platforms are evolving to adopt AR (Augmented Reality)/VR (Virtual Reality)-centric metaverses. Self-driving cars will free up the drivers and passengers to engage in leisure activities such as watching VR videos and playing games while travelling. Before driverless cars can become a “third living space”, open challenges confront the provision of immersive content (e.g., VR videos and gaming) with a high level of Quality of Experience (QoE). Current autonomous vehicular network technologies focus on navigation and safety and rely on ubiquitous high-speed 5G/6G network connectivity to provide a seamless streaming experience in AVs. The compounded risks of cyber- and motion-sickness are identified as barriers to the enjoyment of in-car VR entertainment technologies. The influence of AV car networks in different traffic conditions is identified as a key factor to minimize a person’s risk of sickness and increase overall QoE. However, assessing the QoE of immersive media consumption in AV to its full potential is a difficult task that requires proper standard guidelines and directions. Existing surveys focus only on a specific metaverse or transportation system. These surveys lack a comprehensive view of the entire immersive media consumption process, QoE factors and aspects, and QoE standards for assessing immersive content in vehicular networks. To address these issues, we present an overview of the research landscape in the context of QoE and network standards that highlights the need for experimental studies to inform and consolidate the methodologies and recommendations for standardization of immersive content QoE assessment in AVs. Research and user studies have not yet yielded standards or recommendations describing assessment methodologies to evaluate immersive systems, such as exist for traditional video and images. A framework is outlined to provide a comprehensive understanding of the QoE influencing factors and aspects that will assist in adapting, augmenting, and producing standards for immersive entertainment in autonomous vehicles.

Research on the Application of Computer Big Data Technology in Smart Travel

This paper mainly focuses on the algorithms related to local path planning and path tracking control of unmanned vehicles in the process of obstacle avoidance. By introducing the temporal dimension as a reference, the perceptual results are projected onto the 3D spatio-temporal navigation map by combining the multi-target behavior prediction and other means; by increasing the temporal dimension, the static obstacles and dynamic obstacles are unified into the same parameter space. Under this parameter space, the front-end A* path search initializes the unified B spline curve control points, designs the trajectory cost function and performs nonlinear optimization to generate a spatio-temporal trajectory that satisfies the safety collision-free and vehicle motion constraints (speed and acceleration limits), thus transforming the decision and planning problem under the two-dimensional fence dynamic physical space into a static scene decision and planning problem under the three-dimensional spatio-temporal space. Through simulation verification, the whole process of the proposed trajectory planning method takes 51.27ms on average, which meets the driving requirements of driverless cars. In addition, by adjusting the search conditions of the A* algorithm, its overall planning efficiency is improved by 27.86% compared with the search speed of the traditional algorithm. The actual feeling and data results from the real vehicle experiments show its good tracking effect, which verifies the effectiveness and practicality of the algorithm proposed in this paper.

Autonomous Vehicle Technology and Technology Acceptance: The Role of Technological Readiness on Consumers' Attitudes Towards Driverless Cars and Intention to Use in the Future

Autonomous (driverless) cars, which have entered the automotive industry with the developments in automotive and the advancement of artificial intelligence technologies, are rapidly finding a place in the marketing field. At this point, there are factors affecting consumers' concerns and willingness to use autonomous vehicles. In order to discover these factors, the readiness of consumers and the aspects in which they are ready for this technology are issues that need to be investigated. As a result of this situation, consumers' readiness to use autonomous vehicles, their attitudes toward using them, and their intentions to use them in the future are essential. This study aims to reveal the factors affecting consumers' attitudes and intentions towards using autonomous cars. Research data was collected via an online survey method. The convenience sampling method was used in the research. The research model was tested by structural equation modeling using Smart PLS. As a result of the research, it was found that discomfort and distrust dimensions significantly and negatively affected consumers' attitudes towards usage. It was found that the dimensions of optimism, innovativeness, and anthropomorphism significantly and positively affected consumers' attitudes toward use, and users' attitudes towards use significantly and positively affected their intention to use. The research results show that brands that put autonomous cars on the market should give importance to improvements in the dimensions of optimism, innovation, and anthropomorphism and should make improvements that will eliminate consumers' discomfort and insecurity.

Exploring excitement counterbalanced by concerns towards AI technology using a descriptive-prescriptive data processing method

Given the current pace of technological advancement and its pervasive impact on society, understanding public sentiment is essential. The usage of AI in social media, facial recognition, and driverless cars has been scrutinized using the data collected by a complex survey. To extract insights from data, a descriptive-prescriptive hybrid data processing method is proposed. It includes graphical visualization, cross-tabulation to identify patterns and correlations, clustering using K-means, principal component analysis (PCA) enabling 3D cluster representation, analysis of variance (ANOVA) of clusters, and forecasting potential leveraged by Random Forest to predict clusters. Three well-separated clusters with a silhouette score of 0.828 provide the profile of the respondents. The affiliation of a respondent to a particular cluster is assessed by an F1 score of 0.99 for the test set and 0.98 for the out-of-sample set. With over 5000 respondents answering over 120 questions, the dataset reveals interesting opinions and concerns regarding AI technologies that have to be handled to facilitate AI acceptance and adoption. Its findings have the potential to shape meaningful dialog and policy, ensuring that the evolution of technology aligns with the values and needs of the people.

Panoramic image stitching technology and its application in the field of autonomous driving

Image processing technology is an indispensable part of the development of many fields, in which panoramic image stitching technology is one of the most widely used technologies in image processing, which is not only applied in the frontier fields of technology such as unmanned aerial vehicles and machine vision detection, but is also closely related to people's lives, for example, the panoramic camera shooting function of the smart phone. The driverless car is also a widely concerned field, its development means the liberation of manpower, the realization and improvement of self-driving car technology can alleviate traffic congestion, the use of transportation for people without the ability to drive and many other social problems. This paper will firstly introduce the panoramic collage technology, including its principle, system architecture and application prospect. Secondly, it focuses on the application of panoramic collage technology in the field of self-driving cars, which is divided into two aspects: sensor hardware and collocation algorithm, and analyzes the applicability of different algorithmic techniques in this field based on the demand characteristics of self-driving cars. The research in this paper points out the optimization direction of panoramic collage technology required by self-driving cars, which will be of great value to the development and application of panoramic collage technology in the field of self-driving cars.

Perception of modern technologies by the older generation: comparative analysis

This article discusses the empirical research results devoted to the study of the older generation attitude to modern technologies: fears, expectations, and motivation to use such technologies. The obtained results are interpreted through a humanistic paradigm, in which technologies are considered as a tool for realising human potential, in particular, the potential of the older age group. In the course of the study, a comparative analysis was conducted by gender and age groups. The main conclusions drawn from the scientific work results are as follows. Firstly, it is the older generation that assumes the role of “technological optimists”, unlike younger and middle-aged people who have more concerns about the development of information technology. This applies both to everyday life and to the national problems of the increment of scientific knowledge. Secondly, the older generation noticeably need the humanistic aspect of technology more. This includes distrust of full automation, for example, driverless cars, as well as the need for a tutor and mentor when mastering new technologies. At the same time, it is the elderly generation that expresses the greatest trust in people around them. Thirdly, in many respects, the older generation has more similarities in their expectations and preferences with the younger age group than with those who are closer to them in age.

Object Recognition Using Pattern Analysis

The proposed research paper describes an object System of recognition based on human perception, which is achieved by feature extraction (components) and pattern analysis. Both strategies are applied to each subpart. Feature extraction is done using shaping and pattern analysis is achieved by creating statistical bins for each pattern of vehicle subparts. Each bin contains general and specific measures, and each measure has a certain weight that contributes to the analysis of each bin to decide on similarity or dissimilarity. Object recognition is an important task in computer vision, with applications in robotics, driverless cars, medical image analysis, and many other applications. This article explores the concept of product analysis using benchmarks, highlighting key concepts, methods and recent developments.