Hierarchical Path Planning for Automatic Parking in Constrained Scenarios via Entry-Point Guidance

Advanced Driver Assistance Systems (ADAS) have emerged as a critical technology in enhancing vehicle safety, providing convenience to drivers, and facilitating the transition toward fully autonomous vehicles. The core of these systems relies heavily on computer vision (CV) technologies, which enable vehicles to interpret and understand their surrounding environment in real time. The integration of computer vision in ADAS allows vehicles to recognize and process various objects such as pedestrians, vehicles, traffic signs, and lane markings, significantly improving both safety and the driving experience. In this paper, we explore the pivotal role of computer vision in the development and implementation of ADAS in autonomous vehicles. We begin by reviewing the evolution of ADAS, detailing the different sub-systems such as lane departure warning, automatic emergency braking, adaptive cruise control, and parking assistance, all of which depend on sophisticated image processing techniques. Furthermore, the paper examines the intersection of computer vision with other emerging technologies such as machine learning, deep learning, and sensor fusion. We discuss how these innovations enable ADAS to function more effectively in complex, dynamic environments. The methodology section focuses on cutting-edge algorithms used for object detection, semantic segmentation, and real-time image processing, which are integral to the functionality of modern ADAS. Additionally, we evaluate the challenges that computer vision systems face in real-world applications, including issues with sensor calibration, environmental conditions, computational limitations, and the integration of data from multiple sensors (e.g., cameras, LiDAR, radar). The paper also presents an analysis of the results obtained from various real-world implementations of ADAS technologies, highlighting improvements in driver safety and the reduction of traffic-related accidents. We explore the potential of computer vision in enabling fully autonomous driving by addressing the current gaps in technology and the advancements needed to close these gaps. Finally, the paper concludes with a discussion on the future of ADAS and the role of computer vision in realizing fully autonomous, reliable, and efficient transportation systems. We argue that continued research and development in computer vision, combined with innovations in deep learning and sensor technologies, are critical to advancing the capabilities and reliability of autonomous vehicles in the near future.

BackgroundThe ability to perform a rapid change of direction (COD) is a critical skill in numerous court- and field-based sports. The aim of this review is to investigate the effect of different physical training forms on COD performance.MethodsA systematic review of the literature was undertaken using the following databases: PubMed, SPORTDiscus and Google Scholar. Studies were eligible if they met the following criteria: (1) a COD test measuring performance before and after the training intervention, with specific description of the test in terms of length and number of changes in a direction with specified angles, (2) involve training intervention like plyometric, strength, sprint, specific COD training, or a combination of these training forms targeting the lower extremities, (3) the study had to state training background in terms of which sport they participated in and their competitive level and a detailed methodological description. Non-English articles were excluded. Percentage difference and effect sizes were calculated in order to compare the effects of different training interventions.ResultsA range of studies performing plyometrics, strength, sprint, specific COD training, training with post-activation potentiation or a combination of these training forms were examined. The percentage of change and effect size (ES) were calculated. Seventy-four studies met the inclusion criteria, comprising 132 experimental groups and 1652 unique subjects. The review revealed no clear consensus on which training form is optimal to develop COD performance. All training forms resulted in an increase in performance from almost no ES to large ES.ConclusionsThe results of the study indicate that COD ability is a specific skill, whereas the COD task, the sports require determines which training form is the most effective to develop COD ability. Training targeting improvement in COD performance should address the duration of the training in line with which energy system is utilized. The complexity of the COD task with respect to the individual athlete must be considered. Consequently, the number of changes in direction and the angles of the task are relevant when organizing training.

The introduction to autonomous vehicles and intelligent transportation systems, as well as related work on autonomous vehicles and intelligent transportation systems, are described in this chapter. Aside from that, it has a chapter on autonomous intelligent vehicles and autonomous vehicles. Issues and challenges in autonomous intelligent vehicles, as well as the framework of an intelligent transportation system. In addition, the chapter discusses research opportunities in autonomous intelligent vehicles, IPv4 and IPv6 issues, and the future of IPv9 for vehicles, as well as a survey on autonomous vehicles in highway scenarios. During the last century, the automobile industry has excelled at producing cars that are both safe and affordable. Because of significant advancements in the Internet of things (IoT), the programmable logic controller (PLC), and other aspects of computing, autonomous vehicles (AVs) are becoming a reality. Cars, trucks, and other vehicles that don’t need to be plugged in to get power are referred to as self-sufficient vehicles. The multi-agent-based transportation system, which demonstrates how vehicles, drivers, roads, infrastructure, and vehicles all work together, is one of the most important intelligent transportation systems because it demonstrates how all of these things work together. To solve and manage traffic problems, an intelligent transportation systems (ITS) system employs a variety of technologies. Communication and control, vehicle sensing, and electronics are among these technologies. ITS has been used in the developed world for the past two decades. It is still a novel concept in the developing world. Sensors and computers work together in the Internet of things to store and process data, and data analytics is used to make the traffic system run more smoothly.KeywordsAutonomous vehicle (AV)Intelligent transportation system (ITS)Autonomous intelligent vehicle (AIV)Advanced driver assistance system (ADAS)Advanced driving system (ADS)

<div class="section abstract"><div class="htmlview paragraph">New challenges and complexities are continuously increasing in advanced driver assistance systems (ADAS) development (e.g. active safety, driver assistant and autonomous vehicle systems). Therefore, the health management of ADAS’ components needs special improvements. Since software contribution in ADAS’ development is increasing significantly, remote diagnosis and maintenance for ADAS become more important. Furthermore, it is highly recommended to predict the remaining useful life (RUL) for the prognosis of ADAS’ safety critical components; e.g. (Ultrasonic, Cameras, Radar, LIDAR). This paper presents a remote diagnosis, maintenance and prognosis (RDMP) framework for ADAS, which can be used during development phase and mainly after production. An overview of RDMP framework’s elements is explained to demonstrate how/when this framework is connected to database servers and remote analysis servers. Moreover, Sensors fusion is used in RDMP to detect some sensor failures and even to predict their RUL. Additionally, some well-known machine learning algorithms (MLA) are used to predict RUL of ADAS’ components, and different types of input attributes to these MLA are proposed for some basic ADAS’ components. MLA use training data set, which shall be constructed ideally from actual records reported remotely by RDMP (Prognosis Analysis and Self-Learning System). However, initial dataset before production of the vehicle can be created from ADAS laboratory tests (e.g. Assessments on test tracks), ADAS simulation and theoretical analytical methods. Also, experiments of using the proposed RDMP in some ADAS’ components (Sensor fusion and Braking system as ADAS actuator) are presented. Summary, conclusion with proven results and future work are explained.</div></div>

This paper develops and experimentally demonstrates an automatic parallel parking control algorithm for autonomous ground vehicles (AGVs). The automatic parallel parking problem is described and fuzzy logic controllers are designed for each step of the parking process. Detailed membership functions and fuzzy reasoning rules are given for each fuzzy logic controller. The parking algorithm is tested by simulation and experimental implementation based on the model of a skid steering autonomous ground vehicle. Both results demonstrate the ability of the developed algorithm to parallel park AGVs in tight spaces.

Advanced Driver Assistance Systems (ADAS) are advanced technologies that assist drivers with vehicle operation and navigation. Recent improvements and brisk expansion in the ADAS market, as well as an increase in the frequency of incidents such as sensor spoofing, communication interruption etc., in autonomous vehicles (AVs), have raised the need to research ADAS security technology. The security issues raised by incorporating these technologies into automobiles must be addressed to protect the privacy and safety of passengers and other road users. As a result, the purpose of this research is to investigate the security issues that arise from the integration of ADAS technologies. Addressing these challenges holds the potential to establish a foundation for enhanced safety and dependability within transportation networks amidst the ongoing advancements in vehicle technology. This paper starts by describing the vulnerabilities, threats, assaults, and defense mechanisms of the ADAS. It then delves into the attacks and countermeasures in three categories, namely VANET, Hardware, and Adversarial attacks. VANET attacks encompass threats targeting Vehicular Ad Hoc Networks, aiming to disrupt communication among vehicles or between vehicles and infrastructure. Hardware attacks focus on vulnerabilities within the physical components of ADAS, including sensors, processors, or communication modules. Adversarial attacks involve deliberate manipulations or perturbations introduced into machine learning models or algorithms utilized within ADAS. These attacks aim to deceive or undermine the functionality of AI-based systems, causing misclassification, compromising system integrity, and posing risks to user safety by exploiting vulnerabilities in the AI decision-making process. Finally, this study highlights potential areas for future research, such as the utilization of artificial intelligence (AI), the necessity of industry-wide standardization, and recommends specific future work tailored to each attack described in the corresponding sections.

Preparing drivers for the future: Evaluating the effects of training on drivers’ performance in an autonomous vehicle landscape

In contemporary development, autonomous vehicles (AVs) have emerged as a potential solution for sustainable and smart transportation to fulfill the increasing mobility demands whilst alleviating the negative impacts on society, the economy, and the environment. AVs completely depend on a machine to perform driving tasks. Therefore, their quality and safety are critical concerns for driving users. AVs use advanced driver assistance systems (ADASs) that heavily rely on sensors and camera data. These data are processed to execute vehicle control functions for autonomous driving. Furthermore, AVs have a voice communication system (VCS) to interact with driving users to accomplish different hand-free functions. Some functions such as navigation, climate control, media and entertainment, communication, vehicle settings, vehicle status, and emergency assistance have been successfully incorporated into AVs using VCSs. Several researchers have also implemented vehicle control functions using voice commands through VCSs. If a situation occurs when AV has lost control due to malfunctioning or fault in the installed computer, sensors and other associated modules, driving users can control the AV using voice notes to perform some driving tasks such as changing speeds, lanes, breaking, and directing the car to reach a safe condition. Furthermore, driving users need manual control over AV to perform these tasks in some situations, like lane changing or taking an exit due to divergence. These tasks can also be performed with the help of voice commands using VCSs. Therefore, finding the exact voice note used to instruct different actuators in risk situations is crucial. As a result, VCSs can greatly improve safety in critical situations where manual intervention is necessary. AVs’ functions and quality can be significantly increased by integrating a VCS with an ADAS and developing an interactive ADAS. Now, the driver functions are controlled by voice features. Therefore, natural language processing is utilized to extract the features to determine the user’s requirements. The extracted features control the vehicle functions and support driving activities. The existing techniques consume high computation while predicting the user command and causing a reduction in the AVs’ functions. This research issue is overcome by applying the variation continuous input recognition model. The proposed approach utilizes the linear training process that resolves the listening and time-constrained problems and uncertain response issues. The proposed model categorizes the inputs into non-trainable and trainable data, according to the data readiness and listening span. Then, the non-distinguishable data were validated by dividing it into the linear inputs used to improve the response in the AVs. Thus, effectively utilizing training parameters and the data decomposition process minimizes the uncertainty and increases the response rate. The proposed model has significantly improved the exact prediction of users’ voice notes and computation efficiency. This improvement enhances the VCS quality and reliability used to perform hand-free and vehicle control functions. The reliability of these functions ultimately improves the safety of AVs’ driving users and other road users.

Advanced driver assistance systems (ADAS) have been developed to automate and modify vehicles for safety and better driving experience. Among all computer vision modules in ADAS, 360-degree surround view generation of immediate surroundings of the vehicle is very important, due to application in on-road traffic assistance, parking assistance etc. This paper presents a novel algorithm for fast and computationally efficient transformation of input fisheye images into required top down view. This paper also presents a generalized framework for generating top down view of images captured by cameras with fish-eye lenses mounted on vehicles, irrespective of pitch or tilt angle. The proposed approach comprises of two major steps, viz. correcting the fish-eye lens images to rectilinear images, and generating top-view perspective of the corrected images. The images captured by the fish-eye lens possess barrel distortion, for which a nonlinear and non-iterative method is used. Thereafter, homography is used to obtain top-down view of corrected images. This paper also targets to develop surroundings of the vehicle for wider distortion less field of view and camera perspective independent top down view, with minimum computation cost which is essential due to limited computation power on vehicles.

In two experiments, with a total of 116 male and female undergraduate S s, predictions were tested from the following hypothesized explanation of autokinetic movement (AKM): undetected eye drifts lead to movement of the AKM target across the retina; this movement or error signal leads to the initiation of a cancelling response, and the AKM is directly due to CNS monitoring of the efferent responses or signals sent to the eye muscles with subsequent detection of these signals against a background of efferent noise. The error signal variables studied were stimulus color (yellow or blue-green), stimulus intensity (dim or bright), and stimulus size (1.0 or .1 deg visual angle). Small, dim lights foveally viewed and yellow as opposed to blue-green lights led to significantly more error signals and hence more AKM as measured by mean number of perceived direction changes. Also, increasing retinal displacement by changing viewing angle (from zero to 60 deg) or increasing displacement by increasing diopter strength of prisms (from zero to 30) decreased AKM as measured by number of direction changes, and this effect is due at least in part to the increase in efferent noise caused by the increases in eye muscle tension involved. It was concluded that retinal displacement affects both error signal and noise variables in the AKM and, presumably, in the perception of real movement as well.

In this paper an analytical and geometry-based path planning method is proposed that provides non-holonomic and collision free paths for parking maneuvers. The proposed method is straightforward in the sense that it does not require any sampling or exploring of the configuration space, or relies on any optimization process. Furthermore, the calculation is fast enough to be used in real-time applications for guiding the vehicle in parking scenarios. For any pair of start-goal configurations, the planner presents an infinite set of solutions. These paths have minimum number of driving direction changes as this can be more important for a driver than the path length. They may have any curvature lower than or equal to the maximum specified curvature. Simulations show the superiority of the method in local path planning over conventional methods such as hybrid A* and optimization based approaches in terms of computation time and path quality attributes such as the number of motion primitives and the number of direction changes. To compose a complete motion planning system, the proposed local planner is applied along with hybrid A* as global planner and a path tracking controller. The performance of the motion planning system is evaluated by simulating a valet parking scenario. MATLAB/Simulink is used to simulate the system, and a comprehensive vehicle model in CARSIM is used to represent the vehicle. Simulation results shows the capability of the proposed motion planner to implement automated valet parking tasks while respecting the physical limitations of the vehicle subsystems and driving comfort requirements.

Research on autonomous and smart vehicles technology has been growing exponentially in recent years by integrating more and more smart software inside, those intelligent vehicles have several functionalities such as environment recognition, driver drowsiness detection and communication with other cars, etc. Thus, a supervisor is needed to control sensors and actuators, to manage the network and to process data tasks. In most of smart automobiles, embedded RTOS (Real Time Operating System) are used as supervisor to control all data coming from the different sensors, and to meet the real time requirements. In this paper, we investigate the real time software control in smart/autonomous through the design and prototyping of an ADAS (Advanced Driver Assistant System) real time application for vehicle environment recognition and objects detection. On one hand, we port the real time operating system FreeRTOS on the top of Multi/CPU-FPGA platform, and then we point out the advantages of the FreeRTOS to develop a real smart embedded system for environment car detection within a hardware prototype on a top of the FPGA Zynq hardware platform. On the other hand, a deep comparison between FreeRTOS and standard operating embedded Linaro/Linux, is presented. The entry point of our real time ADAS application system is a point cloud coming from LIDAR sensors as an input, and then several steps are defined in the application: data segmentation, objects searching, features extracts and classification. As a first step of validation, our ADAS application detects pedestrians using High-Definition range data.

Like today's autonomous vehicle prototypes, vehicles in the future will have rich sensors to map and identify objects in the environment. For example, many autonomous vehicle prototypes today come with line-of-sight depth perception sensors like 3D cameras. These cameras are used for improving vehicular safety in autonomous driving, but have fundamentally limited visibility due to occlusions, sensing range, and extreme weather and lighting conditions. To improve visibility and performance, not just for autonomous vehicles but for other Advanced Driving Assistance Systems (ADAS), we explore a capability called Augmented Vehicular Reality (AVR). AVR broadens the vehicle's visual horizon by enabling it to share visual information with other nearby vehicles, but requires careful techniques to align coordinate frames of reference, and to detect dynamic objects. Preliminary evaluations hint at the feasibility of AVR and also highlight research challenges in achieving AVR's potential to improve autonomous vehicles and ADAS.

The article introduces a method of finding optimal routes on raster planes. The method presented takes advantage of a new algorithm that tends to minimize a number of direction changes within a route, while steering clear of the obstacles. Two different schemes, suitable for restricted area Vessel Traffic Service (VTS) system and collision avoidance system located on the own ship are described. The VTS-oriented scheme supports VTS priority policy that may extend or override international give-way regulations. The own-ship routing scheme in a give-way situation is capable of determining the shortest safe path to the destination point. The method takes into account own ship dynamics. It has linear time and space complexities and therefore is sufficiently fast to perform real-time routing on the raster grids. Both the general method and the algorithm it uses are presented in detail in the paper. Implementation issues are also discussed.

Our long-term goal is to employ smartphone-embedded sensors to measure various neurological functions in a patient-autonomous manner. The interim goal is to develop simple smartphone tests (apps) and evaluate the clinical utility of these tests by selecting optimal outcomes that correlate well with clinician-measured disability in different neurological domains. In this paper, we used prospectively-acquired data from 112 multiple sclerosis (MS) patients and 15 healthy volunteers (HV) to assess the performance and optimize outcomes of a Level Test. The goal of the test is to tilt the smartphone so that a free-rolling ball travels to and remains in the center of the screen. An accelerometer detects tilting and records the coordinates of the ball at set time intervals. From this data, we derived five features: path length traveled, time spent in the center of the screen, average distance from the center, average speed while in the center, and number of direction changes underwent by the ball. Time in center proved to be the most sensitive feature to differentiate MS patients from HV and had the strongest correlations with clinician-derived scales. Its superiority was validated in an independent validation cohort of 29 MS patients. A linear combination of different Level features failed to outperform time in center in an independent validation cohort. Limited longitudinal data demonstrated that the Level features were relatively stable intra-individually within 4 months, without definitive evidence of learning. In contrast to previously developed smartphone tests that predominantly measure motoric functions, Level features correlated strongly with reaction time and moderately with cerebellar functions and proprioception, validating its complementary clinical value in the MS app suite. The Level Test measures neurological disability in several domains in two independent cross-sectional cohorts (original and validation). An ongoing longitudinal cohort further investigates whether patient-autonomous collection of granular functional data allows measurement of patient-specific trajectories of disability progression to better guide treatment decisions.