Search Results (71)

In recent years, system intelligence has revolutionized various domains, including the automotive industry, which has fully incorporated intelligence through the emergence of Advanced Driver Assistance Systems (ADAS). Within this transformative context, Autonomous Parking Systems (APS) have emerged as a foundational component, revolutionizing the way vehicles navigate and park with precision and efficiency. This paper presents a comprehensive approach to autonomous parallel parking, leveraging advancements in Artificial Intelligence (AI). Three state-of-the-practice approaches—Imitation Learning (IL), deep Reinforcement Learning (deep RL), and a hybrid deep RL-IL method—are employed and evaluated through extensive experiments in the CARLA Simulator using randomly generated parallel parking scenarios. Results demonstrate that the hybrid deep RL-IL approach achieves a remarkable success rate of 98% in parking attempts, surpassing the individual IL and deep RL methods. Furthermore, the proposed hybrid model exhibits superior maneuvering efficiency and higher overall reward accumulation. These findings underscore the advantages of combining deep RL and IL, representing a significant advancement in APS technology. Full article

With the continuous development of assisted driving technology, the autonomous emergency braking (AEB) system has emerged as a critical innovation in preventing collisions and improving vehicular safety. In this paper, to test the performance of the AEB system efficiently and reliably in real-world driving scenarios, four typical test scenarios for the AEB system were constructed, and five comprehensive performance evaluation indices, including braking parking distance, braking deceleration, collision warning time, speed variation, and accident collision avoidance rate, were proposed for the first time. Subsequently, the Comprehensive Performance Evaluation Index Model (CPEIM) for the AEB system and scoring rules for typical test scenarios were established, which were applied to analyze data obtained from road testing, thereby enabling comprehensive testing and evaluation for AEB system performance. The results showed that the Tesla Model Y and Volvo S90 scored 1.8857 and 2.0433, respectively. Under conditions of dry pavement, across a range of test scenarios, the AEB system of both the Tesla Model Y and Volvo S90 were capable of averting collisions at speeds not exceeding 35 km/h and 45 km/h, respectively. Full article

To address the issues of subjectivity in the selection of design schemes for pocket parks in public building environments, as well as the lack of scientific and objective decision making, a comprehensive TOPSIS method-based optimization decision model for pocket park design schemes was developed. Firstly, an evaluation index system for the pocket park design schemes was established through a literature review and expert interviews. Secondly, the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) were employed to determine the weight coefficients of various evaluation criteria, combining qualitative and quantitative approaches. Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm was applied, constructing a weighted decision matrix for the design schemes based on each criterion. By calculating the distance between each evaluation scheme and the ideal solution, the final ranking was determined, thus completing the comprehensive evaluation and optimization of the design schemes. This model was used to assist designers in the decision-making process for scheme optimization, achieving a priority ranking of five pocket park design schemes in a district of Xi’an, and validating the objectivity, feasibility, and scientific nature of this method. Full article

This paper discusses SESAR’s Auto-Steer Taxi at Airport (ASTAIR) project, which seeks to advance airport ground operations including engine-off taxiing to move towards sustainable airports. The ASTAIR concept integrates human–AI teaming to optimize aircraft movement from gates to runways, with the primary objectives of improving predictability, efficiency, and environmental sustainability at large airports. Building on previous initiatives such as SESAR’s AEON, ASTAIR brings high-level automation to tasks like autonomous taxiing and vehicle routing. The system assists operators by calculating conflict-free routes for vehicles and dynamically adjusting operations based on real-time data. Based on workshops with several stakeholders, we describe the operational challenges involved in implementing ASTAIR, including managing parking stand availability and adapting to unforeseen events. A significant challenge highlighted is the human–automation partnership, where AI plays a supportive role but humans retain control over critical decisions, particularly in cases of system failure. The need for clear and consistent collaboration between AI and human operators is emphasized to ensure safety, efficiency, and improved compliance with take-off schedules, which in turn facilitates in-flight optimization. Full article

Urban parks provide areas for human wellbeing and green space benefits in densely populated landscapes but cannot accomplish all their goals in isolation. They require assistance from collaborations to address challenges. The need for these collaborations is often codified in planning documents. We assisted Rock Creek Park (National Park Service, Washington, D.C.) in their considerations of where to place “partnerships” in their strategic plan by sourcing and summarizing goal topics, hierarchies, and relationships from peer park plans. Using textual coding and network analysis approaches, we examined strategic planning documents from park system entities across the 20 largest urban areas in the United States. We found that, topically, Rock Creek Park’s five initial strategic planning goal topics—safety, access, stewardship, community engagement, and employee engagement—were common and both inward and outward-facing goals. Hierarchically, “partnerships” was routinely considered as a primary goal (a stand-alone topic) and as an integrated secondary goal (supportive within other topics). Additionally, we identified “community building” as an important, outward facing “assistance” goal, differentiated from “partnerships” in audience and encompassing how a park shows up for the urban community and demonstrates its value to the region. We discuss these findings toward urban park planning processes. Full article

Traffic violations are among the leading causes of accidents and significantly compromise urban road safety. This study analyzed traffic violation and incident data collected by automated enforcement systems in urban Beijing from 2019 to 2023, consisting of 3264 traffic accident records and 147,876 traffic violation records. Through a spatiotemporal data association method, 2126 violations directly associated with accidents were identified. The FP-growth algorithm was then applied to derive 18 robust association rules encompassing five categories of accidents and four categories of violations. The findings indicate that the correlation between traffic accidents and violations displays clear peak periods during the morning (8:00–9:00) and evening (17:00–18:00). Violations such as red light running, stopping beyond the stop line during a red light, and ignoring prohibitions strongly correlate with traffic accidents under specific spatiotemporal conditions. Illegally parked vehicles not only reduce road transport efficiency but also significantly elevate the risk of traffic accidents in the surrounding area. The association rules identified in this study can assist traffic managers in formulating more effective measures to mitigate traffic violations, tackle traffic accidents at their source, enhance urban traffic safety, and promote the long-term sustainability of urban transportation systems. Full article

The utilization of contemporary technology enhances the efficiency of parking resource management, contributing to more liveable and sustainable cities. In response to the growing challenges of urbanization, intelligent parking systems have emerged as a crucial solution for optimizing parking management, reducing traffic congestion, and minimizing pollution. The primary aim of this study is to present the concept of the developed web application that supports finding available parking spaces, embodied in the SPARK system (Smart Parking Assistance and Resource Knowledge). The integration of the YOLOv9 (You Only Look Once) segmentation algorithm with Artificial Bee Colony (ABC) optimization, combined with the use of crowdsourced data and deep learning for image analysis, significantly enhances the SPARK system’s operational efficiency. It enables rapid and precise detection of available parking spaces while ensuring robustness and continuous improvement. The accuracy of detecting available parking spaces in the presented system, estimated at 87.33%, is satisfactory compared to similar studies worldwide. Full article

Parks near urban areas provide important opportunities for locals to connect with nature and enjoy outdoor leisure. Climate change planning needs in these parks are pronounced, especially given the large local populations they serve. Ecosystem services, particularly cultural ecosystem services, can frame people’s perceived benefits from these park systems and the larger region. Place attachment on park system and regional scales can differentiate the extent of perceived benefits by the strength of park and regional connection. Together, these can highlight priorities for climate action and communication. The Huron-Clinton Metroparks in densely populated southeast Michigan (US) exemplify an important urban-proximate park system grappling with climate change effects within the parks and across the region. We assisted the Metroparks in creating their Climate Action Plan, including surveying regional residents’ and Metroparks recreationists’ (n = 4069). Here, we examine associations between respondents’ prioritized ecosystem services and levels of place attachment to southeast Michigan and the Metroparks. Results show that on both geographic scales of the park system and region, the three most valued cultural ecosystem services were leisure time spent outdoors, appreciation of beauty, and physical/mental health benefits. However, place attachment level (ambivalent, moderate, or strong) on both scales surfaced ecosystem services prioritization differences and a potentially enhanced role of cultural ecosystem services related to relationships—cultural heritage, social capital, and spirituality—within the Metroparks and with the strong place attached. We discuss these patterns and their connections to the park system and regional climate action planning and communication. Full article

This paper tackles the complex problem of trajectory planning for trucks with multiple trailers, with a specific focus on autonomous parking assistance applications. These systems aim to autonomously guide vehicles from a starting position to a target location while effectively navigating real-world obstacles. We propose a novel six-phase approach that combines global and local optimization techniques, enabling the efficient and accurate generation of reference trajectories. Our method is validated in a case study involving a truck with two trailers, illustrating its capability to handle intricate parking scenarios requiring precise obstacle avoidance and high maneuverability. Results demonstrate that the proposed strategy significantly improves trajectory planning efficiency and robustness in challenging environments. Full article

Traditional engineering education cannot effectively respond to the demand for talents in the construction of smart cities. The application of digital twin technology in education is mostly based on case studies and lacks empirical tests. This study takes the practical teaching of a project-based course on smart city parks as an example to explore the action intention of graduate students to use digital twin technology consistently, and to provide a theoretical basis and teaching practice guidance to promote the rational application of digital twin technology in engineering education. This study set up a quasi-experimental design through the digital twin learning system, grouping 24 graduate students with 4 faculty members. The experimental group is digital twin-assisted practical teaching, and the control group is traditional teaching method, the experimental cycle is 12 weeks, and the total lesson time is 24 h. Secondly, combined with UTAUT2 model and TTF theory, the variable factor hypothesis was adopted as the scale design means, and the experimental validity was improved through questionnaire data analysis. Meanwhile, the influencing factors in the use of digital twin platform were recorded in detail through the process of data collection, data processing and modeling, as well as the application practice of digital twin platform. Finally, the results of the comprehensive survey data show that the graduate students in the experimental group are significantly better than the control group in terms of self-confidence, skill enhancement, learning outcomes, and learning experience. All these results provide information for course teaching practice, training professional teaching teams, optimizing innovative teaching paths, and promoting the cultivation and delivery of smart city technology talents. Full article

The emergence of automotive-grade LiDARs has given rise to new potential methods to develop novel advanced driver assistance systems (ADAS). However, accurate and reliable parking slot detection (PSD) remains a challenge, especially in the low-light conditions typical of indoor car parks. Existing camera-based approaches struggle with these conditions and require sensor fusion to determine parking slot occupancy. This paper proposes a parking slot detection (PSD) algorithm which utilizes the intensity of a LiDAR point cloud to detect the markings of perpendicular parking slots. LiDAR-based approaches offer robustness in low-light environments and can directly determine occupancy status using 3D information. The proposed PSD algorithm first segments the ground plane from the LiDAR point cloud and detects the main axis along the driving direction using a random sample consensus algorithm (RANSAC). The remaining ground point cloud is filtered by a dynamic Otsu’s threshold, and the markings of parking slots are detected in multiple windows along the driving direction separately. Hypotheses of parking slots are generated between the markings, which are cross-checked with a non-ground point cloud to determine the occupancy status. Test results showed that the proposed algorithm is robust in detecting perpendicular parking slots in well-marked car parks with high precision, low width error, and low variance. The proposed algorithm is designed in such a way that future adoption for parallel parking slots and combination with free-space-based detection approaches is possible. This solution addresses the limitations of camera-based systems and enhances PSD accuracy and reliability in challenging lighting conditions. Full article

Car-sharing services, which provide short-term vehicle rentals in urban centers, are rapidly expanding globally but also face numerous challenges. A significant challenge is the effective management of fleet selection to meet user expectations. Addressing this challenge, as well as methodological and literature gaps, the objective of this article is to present an original methodology that supports the evaluation of the suitability of vehicle fleets used in car-sharing systems and to identify the vehicle features preferred by users necessary for specific types of travel. The proposed methodology, which incorporates elements of transportation system modeling and concurrent analysis, was tested using a real-world case study involving a car-sharing service operator. The research focused on the commuting needs of car-sharing users for work or educational purposes. The study was conducted for a German car-sharing operator in Berlin. The research was carried out from 1 January to 30 June 2022. The findings indicate that the best vehicles for the respondents are large cars representing classes D or E, equipped with a combustion engine with a power of 63 to 149 kW, at least parking sensors, navigation, hands-free, lane assistant, heated seats, and high safety standards as indicated by Euro NCAP ratings, offered at the lowest possible rental price. The results align with market trends in Germany, which focus on the sale of at least medium-sized vehicles. This suggests a limitation of small cars in car-sharing systems, which were ideologically supposed to be a key fleet in those kinds of services. The developed methodology supports both system operators in verifying whether their fleet meets user needs and urban policymakers in effectively managing policies towards car-sharing services, including fleet composition, pricing regulations, and vehicle equipment standards. This work represents a significant step towards enhancing the efficiency of car-sharing services in the context of smart cities, where personalization and optimizing transport are crucial for sustainable development. Full article

The increasing prevalence of species that are detrimental to biodiversity is a major concern, particularly for managers of national parks. To develop effective programmes for controlling weeds, it is essential to have a thorough understanding of the extent and severity of infestations, as well as the contributing factors such as temperature, rainfall, and disturbance. Predicting these factors on a regional scale requires models that can incorporate a wide range of variables in a quantifiable manner, while also assisting with on-ground operations. In this study, we present two Bayesian Network models specifically designed for six significant weed species found along the southern coast of Australia. Our models are based on empirical data collected during a coastal weed survey conducted in 2015 and repeated in 2016. We applied these models to the coastal national parks in the isolated and pristine East Gippsland region. Importantly, the prediction models were developed at two different spatial scales that directly corresponded to the scale of the observations. Our findings indicate that coastal habitats, with their vulnerable environments and prevalence of open dune systems, are particularly susceptible to weed infestations. Moreover, adjacent regions also have the potential for colonization if these infestations are not effectively controlled. Climate-related factors play a role in moderating the potential for colonization, which is a significant concern for weed control efforts in the context of global climate change. Full article

This systematic literature review investigates the safety aspects of in-vehicle infotainment systems (IVISs) from 2012 to 2023, analyzing 96 studies. IVISs have significantly evolved, incorporating technologies such as navigation systems, parking assistance, and video games. However, these innovations introduce safety concerns like driver distraction and cognitive overload. This review identifies six primary safety issues: driving distraction, situational awareness, cognitive load, driving performance, interaction success, and emotional state. Head-down displays and touchscreens often have negative safety implications, while speech-based interfaces and Bluetooth-integrated systems are generally considered safer. Suggested improvements include enhancing interface design for touchscreens and exploring gesture-based alternatives. Despite these developments, significant gaps remain in real-world evaluations and studies in diverse driving conditions, highlighting the need for standardized manufacturing norms. Addressing these issues is essential for creating future IVIS that are both reliable and safe. This review serves as a foundation for future research, safety regulations, and design principles aimed at improving IVIS safety. Overcoming these challenges will require a multi-faceted approach that considers user-friendly design, adaptive technologies, and predictive analytics. The goal is to balance technological advancements with road safety, ensuring that IVISs contribute to a safer driving experience without compromising convenience and functionality. Full article

The coupling between modern electric power physical and cyber systems is deepening. An increasing number of users are gradually participating in power operation and control, engaging in bidirectional interactions with the grid. The evolving new power system is transforming into a highly intelligent socio–cyber–physical system, featuring increasingly intricate and expansive architectures. Demands for stable system operation are becoming more specific and rigorous. The new power system confronts significant challenges in areas like planning, dispatching, and operational maintenance. Hence, this paper aims to comprehensively explore potential synergies among various power system components from multiple viewpoints. It analyzes numerous core elements and key technologies to fully unlock the efficiency of this coupling. Our objective is to establish a solid theoretical foundation and practical strategies for the precise implementation of integrated planning and operation dispatching of source–grid–load–storage systems. Based on this, the paper first delves into the theoretical concepts of source, grid, load, and storage, comprehensively exploring new developments and emerging changes in each domain within the new power system context. Secondly, it summarizes pivotal technologies such as data acquisition, collaborative planning, and security measures, while presenting reasonable prospects for their future advancement. Finally, the paper extensively discusses the immense value and potential applications of the integrated planning and operation dispatching concept in source–grid–load–storage systems. This includes its assistance in regards to large-scale engineering projects such as extreme disaster management, facilitating green energy development in desertification regions, and promoting the construction of zero-carbon parks. Full article