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In view of the problems existing in the path planning algorithms of unmanned vehicles, such as low search efficiency, slow convergence speed and easy to fall into the local optimal. Based on the characteristics of route planning for unmanned vehicles, this paper introduces Q-Learning into the traditional ant colony algorithm to enhance the learning ability of the algorithm in dynamic environment, so as to improve the overall efficiency of route search. By mapping pheromones into Q values in Q-learning, rapid search in complex environments is realized, and a collection-free path satisfying constraints is quickly found. The results of case analysis show that compared with the traditional ant colony algorithm and the improved ant colony algorithm considering reward and punishment factors, the improved ant colony algorithm based on Q-Learning can effectively reduce the number of iterations, shorten the path optimization time and path length and other performance indicators, and has many advantages in jumping out of the local optimal, improving the global search ability and improving the convergence speed, and has good adaptability and robustness in complex environments. It ensures the safety and stability of unmanned vehicles in complex environments. [ABSTRACT FROM AUTHOR]

The sixth-generation (6G) technology of mobile networks will establish new standards to fulfill unreachable performance requirements by fifth-generation (5G) mobile networks. This is due to the high requirements for more intelligent network, ultra-lower latency, extreme network communication speed, and supporting massive number of various connected applications. In the long term, the convergence of various business developments with communication platforms, as initiated by 5G, will exaggerate and highlight areas where 5G's capabilities will fall short of performance requirements. Motivated by the development of applications in massive connections, future networks, developments, and technological advancements for mobile communications that go beyond fifth-generation (B5G) networks are being developed. In this context, highly immersive applications are demanded, such as three-dimensional (3D) communications, digital twins, or massive extended reality (XR)/virtual reality (VR) applications, which will need 6G capabilities to be realized at scale to be commercially feasible. Mainly, we anticipate that only the upcoming 6G networks will be capable of running extremely high-performance connectivity with massive numbers of connected devices, even under laborious scenarios such as extreme density, diverse mobility, and energetic environments. In this article, we look at the most recent trends and future emerging trends that are possible to operate 6G network. Paper aims to provide more inclusive and brief review about 6G mobile communication technology in one survey paper. Initially, a comprehensive overview of the 6G system is introduced in terms of visions, drivers, requirements, architecture, and usage scenarios required to enable 6G applications. After that, the opportunities and advantages of 6G mobile technology has been discussed. Further, the promising new techniques that enable 6G technology has been highlighted. This is followed by a potential discussion of challenges and research directions. This article is envisioned to serve as an informative guideline to stimulate interest and further studies for subsequent research and development of 6G networks. Paper will enable the readers to briefly figure out the key requirements, targets, that will be need and the applications, advantages, and opportunities that can be offered as well as the challenges that need to be addressed before the implementation of this new technology. [ABSTRACT FROM AUTHOR]

Smart and autonomous vehicles are inseparable parts of the future Intelligent Transportation Systems (ITS). However, ITS components, and especially its vehicles, are prone to cyber threats. Interconnectivity of different parts, ranging from in-vehicle communication of different modules to vehicle and vehicle to infrastructure message exchanges open a window to the cyber attacks launched through these communication media. This paper introduces the concept of stealth virus or worm in smart and autonomous vehicles which can jeopardize the safety of passengers. Stealth attacks are designed to manipulate a system in a way that while the changes are not detectable by human, the system is negatively influenced over the time. A framework for Intrusion Detection System (IDS) is proposed afterward. The proposed IDS structure is scalable and easily deployable on current and future vehicles which are equipped with Controller Area Network (CAN) buses. Through a case study on car cruise control, a new stealth attack is presented. The attack is analytically discussed first. Then, it is shown how the proposed IDS can detect this kind of threats. [ABSTRACT FROM AUTHOR]

In the process of autonomous vehicle motion planning and to create comfort for vehicle occupants, factors that must be considered are the vehicle's safety features and the road's slipperiness and smoothness. In this paper, we build a mathematical model based on the combination of a genetic algorithm and a neural network to offer lane‐change solutions of autonomous vehicles, focusing on human vehicle control skills. Traditional moving planning methods often use vehicle kinematic and dynamic constraints when creating lane‐change trajectories for autonomous vehicles. When comparing this generated trajectory with a man‐generated moving trajectory, however, there is in fact a significant difference. Therefore, to draw the optimal factors from the actual driver's lane‐change operations, the solution in this paper builds the training data set for the moving planning process with lane change operation by humans with optimal elements. The simulation results are performed in a MATLAB simulation environment to demonstrate that the proposed solution operates effectively with optimal points such as operator maneuvers and improved comfort for passengers as well as creating a smooth and slippery lane‐change trajectory. [ABSTRACT FROM AUTHOR]

Road region and non-road region separation in the unstructured road intends to be an important task for safe navigation and collision avoidance for autonomous driving vehicles. The road that connects rural areas and cities to the national highways are considered as unstructured roads. Absence of clear lane marking on these unstructured roads makes them more prone to accidents in comparison to highways, which have clear lane marking for indication of road and non-road regions. However, the unstructured roads have different color information from its background that paves an easy way for design and development of an efficient road detection system for recognition and classification of road and non-road regions. Hence, in this paper, we propose an efficient road detection system for the classification of unstructured roads into road and non-road regions using multiple nearest neighbors (NN) classifier and soft voting aggregation approach. The proposed system utilized the chromatic information (i.e. *a,*b, and Hue) to train the NN classifiers and aggregated their output using soft voting (SV) approach for final output response. The output results of multiple classifiers were aggregated using SV approach based on the confidence score obtained by each individual classifier. The performance of the proposed system is evaluated in terms of precision, recall, accuracy, intersection over union (IOU), true positive rate (TPR), and processing time and compared with current state of art methods reported in the literature. The proposed system achieved precision, recall, accuracy, IOU, and TPR of 96.79%, 96.92%, 97.8%, 96.08% and 96%, respectively with the processing time three times smaller than those of the existing state of art methods. The experimental results demonstrate that the proposed system can provide an effective guidance to the autonomous vehicles through recognition and classification of road and non-road regions in rural, urban, and city areas, wherein single unstructured roads connect the national highways. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Connected and autonomous vehicles produce, store and communicate a large amount of personal data (the route taken, the stop points, home and work addresses, etc.). The development of this type of vehicles brings the opportunity to offer new services to road users, with more software and hardware components on the vehicle. Many of these components suffer from weaknesses that can be exploited. The issue is that a single vulnerability in one element of the system will threaten the privacy of the vehicle's users (The weakest link principle of IT security). In this paper, we present a privacy threat analysis on the general architecture of connected and autonomous vehicles, to point out privacy risks according to formal privacy requirements. We present a use case modularization and its analysis that helps us to discern the privacy requirements of this use case, which can be enabled by manufacturers. To meet the previous objectives, we follow a LINDDUN methodology of privacy threat analysis [ABSTRACT FROM AUTHOR]

Autonomous Vehicle Systems are committed to safer, more efficient and more convenient transportation on the roads of the future. However, concerns about vehicle data privacy and security remain significant. Federated Learning, as a decentralized machine learning approach, allows multiple devices or data sources to collaboratively train models without sharing raw data, providing essential privacy protection. In this paper, we propose a privacy-preserving framework for autonomous vehicles, named FLAV. First, we use a multi-chain parallel aggregation strategy to transmit model parameters and design a model parameter filtering mechanism, which reduces communication overhead by filtering out the local model parameters of certain vehicles, thereby alleviating bandwidth pressure. Second, we introduce a dynamic adjustment mechanism that automatically adjusts regularization strength by comparing each vehicle's local parameters with the cumulative parameters of preceding vehicles in the chain. This mechanism balances local training with global consistency, ensuring the model's adaptability to local data while improving coordination between vehicles in the chain. Experimental results demonstrate that our proposed method reduces communication costs while improving model accuracy and privacy protection level, effectively ensuring the security of autonomous driving data. [ABSTRACT FROM AUTHOR]

The scientific paper presents a practical research carried out by the authors in order to implement certain concepts of technical nature, so that those interested in this field can become aware of a certain way of exposing the problems regarding the role of technology and autonomous vehicles in the sustainable development of car transportation. According to previous research, it has been found that human error has a share of about 95% in the occurrence of road accidents in Europe, causing thousands of human lives to be taken away annually. Experts in the field say that autonomous vehicles can significantly reduce this percentage and at the same time contribute to increasing road safety. Digital technology also has its advantages and disadvantages. It helps reduce congestion and pollution in road traffic, increases access to mobility for people with disabilities, creates new jobs, brings economic growth, increases safety in road traffic but can also become dangerous when disturbances and malfunctions make their mark on artificial intelligence. We present further a technical and managerial study with engineering nuances, through which readers can look at and understand the concept of sustainable development in a global context, presenting the place, role and current stage of development of the autonomous vehicle in the road transport system. At the end, some conclusions and further research directions in the approached field are presented. [ABSTRACT FROM AUTHOR]

Self-driving cars, presently under on road testing stage, contain software to drive the vehicle without intervention of a human driver. The two major competencies of this software are perception and planning. When any discrepancy is detected in any of these competencies, it causes "failure in operation" or "disengagement" i.e., control is handed over to human driver present as a back-up arrangement. In this paper, on-road testing data is considered for self-driving cars in California (USA) for eight manufacturers whose vehicles have been tested for at least 10,000 miles. The various reasons for reported disengagement or failure have been attributed to error in two software competencies of self-driving cars - perception competency or planning competency. The "number of miles driven" has been taken as a dependent parameter while cumulative failure due to perception or due to planning have been taken as an independent variable. The seven NHPP software reliability failure-count models have been compared to identify the best-fit model for number of miles driven against failure due to perception discrepancy and number of miles driven against failures due to planning discrepancy for the above-mentioned eight selfdriving car manufacturers. The type of debugging has been identified based on the nest fit model and future predicted values of the respective failures are estimated. [ABSTRACT FROM AUTHOR]

This paper presents a conceptual trust model for the perceptual sensor data of autonomous vehicles to improve their reliability and safety of operation. In particular, the model aims to ensure the trustworthiness of the data used in the automated decision-making processes of vehicles. The presented model forms a comprehensive view of factors that affect trust in the perceptual sensor data. These factors, namely environment, operation, security, and technical limitations, further cover a number of related trust parameters used to evaluate trust values. Each parameter is evaluated using Dempster-Shafer theory using a trust-distrust frame. Mass values are based on instantaneous sensor data and sensor behaviour tested under similar circumstances. As a result, this model is viable for use in conjunction with data fusion and decision-making to improve their reliability by enabling the tracking of the development of sensor trustworthiness. [ABSTRACT FROM AUTHOR]

This paper presents an intervehicle distance control (IDC) to solve the problem of autonomous vehicle platooning, motivated by future automated highway system (AHS) or smart road which is proposed as intelligent transportation system (ITS) technology. First the velocity and position control of the single vehicle is studied based on internal model compensator. And then the platooning problem on multiple vehicles is solved in the light of multiagent concept. Moreover, the platoon condition is derived for the corresponding scheme. Further we analyze the influence of controller parameters on the whole system, and propose the guidance for parameter design. Finally some simulations are used to verify the effectiveness of the proposed IDC scheme with an analysis on controller parameters. [ABSTRACT FROM AUTHOR]

The Embedded Higher-order Agent (E-HoA) architecture presented in this paper addresses the growing need for context-aware ambient systems, such as autonomous vehicles. This multi-process architecture, to be embedded into the control loop of these vehicles, includes a Belief-Desire-Intention agent that can consistently assist the symbolic execution of intentions. It also performs the appropriate conversion of these intentions into real physical vehicle maneuvers based on ROS. The proposed architecture offers gradually 4 levels of reactivity, from arch-reflex to the deep modification of the previously built symbolic execution plan. The presented use-case, the daily delivery of a network of pharmacy offices by an autonomous vehicle taking into account contextual (spatio-temporal) traffic features, shows the efficiency and the modularity of the architecture, as well as the scalability of the reaction levels. [ABSTRACT FROM AUTHOR]

This paper contributes to a growing discussion on how communities plan to prepare for new mobility opportunities offered by technological innovations, specifically shared mobility services, electric vehicles, connected and autonomous vehicles, and Mobility as a Service. Literature in this field predominantly concentrates on technology and operation, market research and impact assessment. There is a clear gap in understanding how practitioners anticipate the planning considerations and research needs to prepare for the transformation of the transportation system. Taking a participatory approach, this research attempts to fill this gap through focus group sessions that capture the perspectives of practicing planners. The issues identified by the participants of this study include regulations/policy for emerging technologies, standardization of infrastructure design, rethinking land use planning, and foreseeing economic benefit and equity issues. This study reveals a set of planning considerations ranging from infrastructure provisions in the short-term to an adaptive approach in writing policy for the long-term. Given the uncertainty of newer technologies, the participants emphasized that practitioners need more information, such as the evolution of technologies, land use implications and design potentials. [ABSTRACT FROM AUTHOR]

• Proposes a sustainable AV-dedicated lane and pricing policy (SALP) framework. • The framework minimizes the total travel time cost, emissions cost, and electricity cost. • Two strategies are proposed to alleviate the social inequity issues. • Proposes framework is an effective program to increase the AV market penetration rate. • Experiments show significant reductions in travel time, emissions, and electricity costs. Autonomous vehicles (AVs) show promise for increasing roadway safety and capacity. During the AV transition era, which will be characterized by a mixed fleet of AVs and human-driven vehicles (HDVs), it is expected that the allure of these prospective benefits will motivate road agencies to allocate AV-dedicated lanes. This paper proposes a sustainability-driven AV-dedicated lane and pricing policy (SALP) framework that addresses the three pillars of sustainable development—social, environmental, and economic. The framework is formulated as a bi-level problem where the upper-level model yields decisions on the timing, location, and quantity of AV-dedicated lanes and tolling levels to minimize total travel time, emissions, and electricity consumption costs (that is, the economic and environmental pillars). To alleviate potential inequity (the social pillar), two considerations are proposed: revenue neutrality to compensate for the increase in travel costs of travelers and an equity constraint to limit the exacerbation of HDV travel costs. At the lower level, travelers react to the decisions made at the upper level by choosing their vehicle types (AV vs. HDV) and routes. The SALP is solved using Genetic and Frank-Wolfe algorithms. The results of the numerical experiments suggest that the proposed SALP addresses all three pillars, as it yields significant reductions in total travel time, emissions, and electricity costs. [ABSTRACT FROM AUTHOR]

• AV would significantly influence urban transportation systems and human mobility. • AVs would encourage dispersed urbanization, reduce parking demand, and enhance network capacity. • AVs would reduce energy use and protect the environment by reducing GHG emissions. • AVs would reduce traffic crashes and increase passengers' convenience and productivity. • The study identifies existing research gaps and proposes directions for further research. The article discusses the short, medium, and long-term effects of Autonomous Vehicles (AVs) on the urban transportation and environment by means of a systematic review of the extant literature on the subject matter. A corpus of 130 articles was collected from multiple sources using selected keywords. The review critically analyzes key findings of these papers in the light of a SWOT (Strength, Weakness, Opportunity, and Threat) analysis. Although the technology remains to be commercially deployed, broad consensus is found in the literature. First, AV would influence urban transportation and human mobility by reducing vehicle ownership, public and active travel, traffic delay and congestion, travel costs, and by increasing accessibility, mobility, Vehicle Miles Traveled, and revenue generation for commercial operators. Second, AVs would have long-term effects by encouraging dispersed urban development, reducing parking demand, and enhancing network capacity. Third, AVs would reduce energy consumption and protect the environment by reducing Greenhouse Gas emissions. Fourth, AVs would reduce traffic crashes involving human errors and increase the convenience and productivity of passengers by facilitating for multitasking. However, most people are very concerned about personal safety, security, and privacy. Finally, the study identifies critical research gaps and advances priority directions for further research. [ABSTRACT FROM AUTHOR]

Autonomous vehicle technology based on information technology and software will lead the automotive industry in the near future. Vehicle localization technology is a core expertise geared toward developing autonomous vehicles and will provide location information for control and decision. This paper proposes an effective vision-based localization technology to be applied to autonomous vehicles. In particular, the proposed technology makes use of numerical maps that are widely used in the field of geographic information systems and that have already been built in advance. Optimum vehicle ego-motion estimation and road marking feature extraction techniques are adopted and then combined by an extended Kalman filter and particle filter to make up the localization technology. The implementation results of this paper show remarkable results; namely, an 18 ms mean processing time and 10 cm location error. In addition, autonomous driving and parking are successfully completed with an unmanned vehicle within a 300 mx 500 m space. [ABSTRACT FROM AUTHOR]

Highly automated and fully autonomous vehicles are much more likely to be accepted if they react in the same way as human drivers do, especially in a hybrid traffic situation, which allows autonomous vehicles and human-driven vehicles to share the same road. This paper proposes a human-like motion planning model to represent how human drivers assess environments and operate vehicles in signalized intersections. The developed model consists of a pedestrian intention detection model, gap detection model, and vehicle control model. These three submodels are individually responsible for situation assessment, decision making, and action, and also depend on each other in the process of motion planning. In addition, these submodels are constructed and learned on the basis of human drivers' data collected from real traffic environments. To verify the effectiveness of the proposed motion planning model, we compared the proposed model with actual human driver and pedestrian data. The experimental results showed that our proposed model and actual human driver behaviors are highly similar with respect to gap acceptance in intersections. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to formally define and solve ethical problems of how an artificial vehicle (AV) determines its driving behavior when there are some passengers in the AV and some pedestrians on a street. We construct a mathematical model introducing mainly two Bentham- and Nash-types social welfare functions, and derive optimal solutions. We show the optimal solutions are completely different depending on the functions and their parameters. Our contribution is that policymakers or managers of AVs can discuss the problem and determine an algorithm for autonomous driving by formalizing the situation and offering the optimal solutions. [ABSTRACT FROM AUTHOR]

• The qualitative study, which combines semi-structured interviews and design metaphors, is introduced. • The eight different Gen Z mindsets on their in-car entertainment experience are observed. • The potential implications of mindsets on an autonomous vehicle's entertainment experience were discussed. Alongside the development of digital life, certain concepts gain new dimensions among new generations. When entertainment is the subject, it creates emergent desires, behaviours and mindsets in the social and consumer cultures. However, existing perspectives around in-car entertainment do not fully embrace the digitally dynamic environment in which Generation Z is heavily involved. This paper uses the future autonomous vehicle's interior context as a case study to understand Gen Z's meaning change around in-car entertainment. It draws on a qualitative research methodology by combining semi-structured interviews with design metaphor activities to understand Gen Z's altered mindset with the impact of digital technology. As ultimate findings, eight different in-car entertainment mindsets of Gen Z emerged: Effortlessness Seeking, Sense of Belonging, Balance Seeking, Authenticity Seeking, Advocating, Decision-Making, Creating and Interacting. The implications of an autonomous vehicle's entertainment experience were discussed. It is expected that this study will contribute to understanding the shifting mindset and behaviour of Gen Z in how they desire to entertain themselves within an autonomous vehicle. The study suggests what to consider regarding maximising in-car entertaining experiences for Gen Z. [ABSTRACT FROM AUTHOR]

In this paper, to reduce the congestion rate at the city center and increase the traveling quality of experience (QoE) of each user, the framework of long-range autonomous valet parking is presented. Here, an Autonomous Vehicle (AV) is deployed to pick up, and drop off users at their required spots, and then drive to the car park around well-organized places of city autonomously. In this framework, we aim to minimize the overall distance of AV, while guarantee all users are served with great QoE, i.e., picking up, and dropping off users at their required spots through optimizing the path planning of the AV and number of serving time slots. To this end, we first present a learning-based algorithm, which is named as Double-Layer Ant Colony Optimization (DLACO) algorithm to solve the above problem in an iterative way. Then, to make the fast decision, while considers the dynamic environment (i.e., the AV may pick up and drop off users from different locations), we further present a deep reinforcement learning-based algorithm, i.e., Deep Q-learning Network (DQN) to solve this problem. Experimental results show that the DL-ACO and DQN-based algorithms both achieve the considerable performance. [Display omitted] • We aim to optimize path planning of AV and minimize the overall distance. • We propose two solutions, i.e., DL-ACO and DQN-based algorithms. • The DL-ACO can be applied to a new scenario or unfamiliar environment. • The DQN-based model can be applied to scenarios where prior training can be carried out. [ABSTRACT FROM AUTHOR]

Automation with inherent artificial intelligence (AI) is increasingly emerging in diverse applications, for instance, autonomous vehicles and medical assistance devices. However, despite their growing use, there is still noticeable skepticism in society regarding these applications. Drawing an analogy from human social interaction, the concept of trust provides a valid foundation for describing the relationship between humans and automation. Accordingly, this paper explores how firms systematically foster trust regarding applied AI. Based on empirical analysis using nine case studies in the transportation and medical technology industries, our study illustrates the dichotomous constitution of trust in applied AI. Concretely, we emphasize the symbiosis of trust in the technology as well as in the innovating firm and its communication about the technology. In doing so, we provide tangible approaches to increase trust in the technology and illustrate the necessity of a democratic development process for applied AI. [ABSTRACT FROM AUTHOR]

An autonomous valet parking (AVP) system is designed to locate a vacant parking space and park the vehicle in which it resides on behalf of the driver, once the driver has left the vehicle. In addition, the AVP is able to direct the vehicle to a location desired by the driver when requested. In this paper, for an AVP system, we introduce technology to recognize a parking space using image sensors. The proposed technology is mainly divided into three parts. First, spatial analysis is carried out using a height map that is based on dense motion stereo. Second, modelling of road markings is conducted using a probability map with a new salient-line feature extractor. Finally, parking space recognition is based on a Bayesian classifier. The experimental results show an execution time of up to 10 ms and a recognition rate of over 99%. Also, the performance and properties of the proposed technology were evaluated with a variety of data. Our algorithms, which are part of the proposed technology, are expected to apply to various research areas regarding autonomous vehicles, such as map generation, road marking recognition, localization, and environment recognition. [ABSTRACT FROM AUTHOR]

In this paper, a steering control system for the path tracking of autonomous vehicles is described. The steering control system consists of a path tracker and primitive driver. The path tracker generates the desired steering angle by using the look-ahead distance, vehicle heading, and a lateral offset. A method for applying an autonomous vehicle to path tracking is an advanced pure pursuit method that can reduce cutting corners, which is a weakness of the pure pursuit method. The steering controller controls the steering actuator to follow the desired steering angle. A servo motor is installed to control the steering handle, and it can transmit the steering force using a belt and pulley. We designed a steering controller that is applied to a proportional integral differential controller. However, because of a dead band, the path tracking performance and stability of autonomous vehicles are reduced. To overcome the dead band, a dead band compensator was developed. As a result of the compensator, the path tracking performance and stability are improved. [ABSTRACT FROM AUTHOR]

With the widespread application of deep learning methodologies, many fields including Intelligent Transportation Systems (ITS) have integrated neural network-based models. In return, the promising performance of neural network-based models attracts more research efforts being paid to the deep learning area. Autonomous vehicles, as the future ITS participants, have achieved tremendous development with the help of convolutional neural network (CNN)-based detectors (e.g., vehicle detectors, lane detectors, pedestrian detectors, traffic sign detectors, etc.) in recent years. However, we have noticed that researchers leveraged different computing power when publishing their experimental results, which could lead to unfair comparisons. In this paper, we focus on CNN-based pedestrian detectors. We conduct a comprehensive comparative study of the representative CNN-based pedestrian detectors, aiming to investigate the influence of experimental settings by eliminating the bias of different experimental environments. [ABSTRACT FROM AUTHOR]

• Vulnerable road users' perceptions of autonomous vehicles (AVs) in 2017 and 2019 in Pittsburgh were compared. • Interactive experiences with AVs increased from 2017 to 2019. • Perceived safety of AVs increased from 2017 to 2019, while AVs' receptivity did not change significantly. • Interactive experiences with AVs positively affected vulnerable road users' perceptions. • We recommend policymakers consider the benefits and safety of vulnerable road users in AV testing. Autonomous vehicles (AVs) are expected to eliminate many driver errors to save thousands of lives. As vulnerable road users, pedestrians and bicyclists are more likely to be injured or killed in accidents involving cars. Therefore, understanding the perceptions of vulnerable road users is crucial for developing and deploying AVs and solving public concerns because the acceptance of AVs relies on both AV users and other road users, such as vulnerable road users. This paper analyzed two surveys collected in 2017 and 2019 by BikePGH in Pittsburgh to understand vulnerable road users' perceptions of AVs in different years. The analysis showed that vulnerable road users' interactive experiences with AVs increased from 2017 to 2019 in Pittsburgh, and the interactive experiences with AVs positively affected vulnerable road users' perceived safety and receptivity toward AVs in 2017 and 2019. Specifically, vulnerable road users' perceived safety of AVs significantly increased from 2017 to 2019, while their receptivity toward AVs did not change significantly during that period. Additionally, autonomous driving accidents negatively affected vulnerable road users' perceptions of AVs. Therefore, we recommend that policymakers provide opportunities for the public to interact with AVs and guarantee vulnerable road users' safety and benefits in AV testing. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this paper is the design and development of an autonomous vehicle which would be able to harvest saffron flowers unsupervised. This vehicle will be able to collect the flowers of the saffron unharmed. The time for the harvest isn’t more than the time needed by a human to accomplish the same task. Moreover, this procedure must be more cost effective compared to the same task accomplished by a human. We also take under consideration that the plant appears to be very sensitive to the fluctuation of pH and can be chemically destroyed in the presence of oxidizing factors and in an ionized environment. It is also very sensitive to the atmosphere's humidity (due to its trichoid stamen) however it seems to be resistant to heat. Generally, the vehicle will be able to accomplish all the operations without the need of human supervision. [Copyright &y& Elsevier]

Abstract: Humankind has given itself the scientific name Homo sapiens (man the wise) because of our mental capacities are so important to our everyday lives and our sense of self. The requirement inspires to create new things. The basic machineries that makes our life simple and getting combined together to make the facilities for life more simple. This paper deals with the designing of the automatic guided vehicles enabled with artificial intelligence, to increase the efficiency and reduce time for searching any physical objects. The developed system prototype, a twin dc motor operated small vehicle has been interfaced with PC through standard USB port. Data from the vehicle is sent towards the PC through a transmission line using serial data transmission in the form of data stream. In software part, for searching the target as physical object, the image analysis of the object is accomplished using proposed near color value search method. The vehicle module is equipped with only one optical sensor i.e a standard web camera. This type of vehicle can be used in variety of application such as military spying, firefighting system, remote and autonomous exploration, home security etc. [Copyright &y& Elsevier]

Abstract: Trajectory tracking is one of the important aspect in autonomous vehicle. Therefore, this paper proposed a stable tracking control for autonomous vehicle. An approach that consists of steering wheel control and lateral control is introduced. This control algorithm is used for nonholonomic navigation problem which is tracking a reference trajectory in a close loop form. Future prediction point is used to calculate the vehicle lateral error is introduced. Controller based on steering wheel angle and yaw rate of the vehicle is introduced. The controller consists of relationship between future point lateral error, linear velocity, heading error and reference yaw rate. The effectiveness of the proposed controller is demonstrated by using simulation. [Copyright &y& Elsevier]

• Level 5 fully autonomous vehicle pods (AVPs, driverless vehicles) approach reality. • Identifies four necessary and five combinations of traits predicting adoption. • Throws light on motivational mechanisms of consumers' adoption of new technology. • Implications for designing, manufacturing and commercializing innovative products. • Significance of elements congruent with target customers' personality traits. Increasing technological innovation means level 5 fully autonomous vehicle pods (AVPs) that do not require a human driver are approaching reality. However, the adoption of AVPs continues to lag behind predictions. In this paper, we draw on Mowen's (2000) 3M model taking a multi-analytical approach utilising PLS-SEM and fuzzy set qualitative comparative analysis, to investigate how personality trait sets motivate consumers to adopt AVPs. Based on a survey of 551 US respondents , we identify four necessary traits and five combinations of traits that predict adoption. We contribute to consumer psychology theory by advancing the understanding of the motivational mechanisms of consumers' adoption of autonomous vehicles that are triggered and operationalised by personality traits and conceptualising innovativeness as a complex multidimensional construct. From a managerial perspective, our findings highlight the significance of incorporating elements that are congruent with target customers' personality traits, when designing, manufacturing and commercializing innovative products. [ABSTRACT FROM AUTHOR]

Autonomous vehicle driving systems (AVDSs) have independent decision structures from the users to manage and control the operations of the vehicles both in normal conditions and unexpected situations. Although there are some advantages, such as decreasing accidents reasoned by human errors and efficient energy usage, it is obvious that some risks are rooted in this technology usage. Therefore, it will be beneficial to realize a risk assessment application for autonomous vehicles (AVs) and/or driving systems (DSs) since whose risks are crucial to test and solve. In this paper, a multi-criteria decision making (MCDM) methodology integrating DEcision MAking Trial and Evaluation Laboratory (DEMATEL), Analytical Network Process (ANP), and VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) techniques under spherical fuzzy environment have been suggested to evaluate AVDS alternatives in terms of considered risk criteria. Spherical fuzzy sets (SFS), which are the extension of the ordinary fuzzy sets, have been used to consider the hesitancy of experts and decision-makers as well as uncertainty and impreciseness in the available data. In the application, six AVDS alternatives have been evaluated in terms of seven main criteria and forty sub-criteria. The factors "Software Specifications" and "Reliability" have been determined as the most important main and sub-criteria with the weights 0.193 and 0.066, respectively. Additionally, comparative and sensitivity analyses have been applied to present flexibility, validation and verification of the proposed methodology together with the sensitivity of the given decisions. Based on the application results and conducted analyses, possible implications by views of theoretical, managerial, and policy context have been discussed. [Display omitted] • Risk assessment of autonomous vehicle driving systems is critical and essential. • It should be evaluated in a multi-dimensional approach considering many perspectives. • These evaluations are carried out based on the experts' and decision makers' knowledge. • These evaluations consist of uncertainty specified as impreciseness and hesitancy. • Spherical fuzzy sets are used to enhance sensitivity and flexibility of the results. [ABSTRACT FROM AUTHOR]

Finding a pathway between crop rows is essential for automated guidance of some agricultural vehicles. The research reported in this paper developed a vision-based method for detecting crop rows. This method applied the Hough transform and connectivity analysis to process images of a vehicle's forward view and to use them to find the appropriate pathway in the field. The Hough transform was used to detect crop rows and the connectivity analysis was applied to identify the most suitable path from all possible choices. This system was implemented in an agricultural tractor and tested in both laboratory and field experiments. The methodology devised overcame image noise problems and successfully determined the proper trajectory for the tractor. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) attract a lot of attention recently. They are expected to assist/replace the human drivers in maneuvering the vehicle, thereby reducing the likelihood of road accidents caused by human error, as a means to improve the road traffic safety. However, AVs have their inherent safety and security challenges, which have to be addressed before they are ready for wide adoption. This paper presents an overview of recent research on AV safety failures and security attacks, as well as the available safety and security countermeasures. [ABSTRACT FROM AUTHOR]