Optimized Parameters for Over-Height Vehicle Detection under Variable Weather Conditions

Over-height vehicle drivers continuously ignore warning signs and strike onto bridges despite the number of preventative methods installed at low clearance bridges. In this paper, the authors present a new method for over-height vehicle strike prevention with a single calibrated camera mounted on the side of the roadway. The camera is installed at the height of the $\textit{over-height plane}$ formed by the average of the maximum allowable heights across all lanes in a given traffic direction; the error caused by the road gradient is assumed to be negligible and absorbed through the calibration process. At that height, the over-height plane can be safely approximated as a line in the camera view. Any vehicle exceeding this line is consequently over-height. The camera position and orientation are determined through a calibration process proposed. Instances of over-height vehicles are detected through optical flow monitoring. Evaluation of the system resulted in a height accuracy of ±2.875  mm; outperforming the target accuracy of ±5 cm, OH detection accuracy of 68.9%, and classification performance of 83.3%. Although its accuracy is comparable to existing laser beam systems, it outperforms them on cost which is an order of magnitude less because of eliminating the need for new permanent infrastructure.This material is based upon work supported by London Under- ground (TfL)–Redbridge Council, Cambridge Centre for Smart Infrastructure (CSIC), Marie Curie International Research Staff Exchange Scheme (IRSES), Cambridge Overseas Trust, and Georgia Institute of Technology

Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems

Intelligent transportation systems (ITSs) have been fueled by the rapid development of communication technologies, sensor technologies, and the Internet of Things (IoT). Nonetheless, due to the dynamic characteristics of the vehicle networks, it is rather challenging to make timely and accurate decisions of vehicle behaviors. Moreover, in the presence of mobile wireless communications, the privacy and security of vehicle information are at constant risk. In this context, a new paradigm is urgently needed for various applications in dynamic vehicle environments. As a distributed machine learning technology, federated learning (FL) has received extensive attention due to its outstanding privacy protection properties and easy scalability. We conduct a comprehensive survey of the latest developments in FL for ITS. Specifically, we initially research the prevalent challenges in ITS and elucidate the motivations for applying FL from various perspectives. Subsequently, we review existing deployments of FL in ITS across various scenarios, and discuss specific potential issues in object recognition, traffic management, and service providing scenarios. Furthermore, we conduct a further analysis of the new challenges introduced by FL deployment and the inherent limitations that FL alone cannot fully address, including uneven data distribution, limited storage and computing power, and potential privacy and security concerns. We then examine the existing collaborative technologies that can help mitigate these challenges. Lastly, we discuss the open challenges that remain to be addressed in applying FL in ITS and propose several future research directions

Advanced Sensing and Control for Connected and Automated Vehicles

Connected and automated vehicles (CAVs) are a transformative technology that is expected to change and improve the safety and efficiency of mobility. As the main functional components of CAVs, advanced sensing technologies and control algorithms, which gather environmental information, process data, and control vehicle motion, are of great importance. The development of novel sensing technologies for CAVs has become a hotspot in recent years. Thanks to improved sensing technologies, CAVs are able to interpret sensory information to further detect obstacles, localize their positions, navigate themselves, and interact with other surrounding vehicles in the dynamic environment. Furthermore, leveraging computer vision and other sensing methods, in-cabin humans’ body activities, facial emotions, and even mental states can also be recognized. Therefore, the aim of this Special Issue has been to gather contributions that illustrate the interest in the sensing and control of CAVs

Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges

Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles (CAV), including perception, planning, and control. However, its reliance on vehicular data for model training presents significant challenges related to in-vehicle user privacy and communication overhead generated by massive data volumes. Federated learning (FL) is a decentralized ML approach that enables multiple vehicles to collaboratively develop models, broadening learning from various driving environments, enhancing overall performance, and simultaneously securing local vehicle data privacy and security. This survey paper presents a review of the advancements made in the application of FL for CAV (FL4CAV). First, centralized and decentralized frameworks of FL are analyzed, highlighting their key characteristics and methodologies. Second, diverse data sources, models, and data security techniques relevant to FL in CAVs are reviewed, emphasizing their significance in ensuring privacy and confidentiality. Third, specific and important applications of FL are explored, providing insight into the base models and datasets employed for each application. Finally, existing challenges for FL4CAV are listed and potential directions for future work are discussed to further enhance the effectiveness and efficiency of FL in the context of CAV

Actuators for Intelligent Electric Vehicles

This book details the advanced actuators for IEVs and the control algorithm design. In the actuator design, the configuration four-wheel independent drive/steering electric vehicles is reviewed. An in-wheel two-speed AMT with selectable one-way clutch is designed for IEV. Considering uncertainties, the optimization design for the planetary gear train of IEV is conducted. An electric power steering system is designed for IEV. In addition, advanced control algorithms are proposed in favour of active safety improvement. A supervision mechanism is applied to the segment drift control of autonomous driving. Double super-resolution network is used to design the intelligent driving algorithm. Torque distribution control technology and four-wheel steering technology are utilized for path tracking and adaptive cruise control. To advance the control accuracy, advanced estimation algorithms are studied in this book. The tyre-road peak friction coefficient under full slip rate range is identified based on the normalized tyre model. The pressure of the electro-hydraulic brake system is estimated based on signal fusion. Besides, a multi-semantic driver behaviour recognition model of autonomous vehicles is designed using confidence fusion mechanism. Moreover, a mono-vision based lateral localization system of low-cost autonomous vehicles is proposed with deep learning curb detection. To sum up, the discussed advanced actuators, control and estimation algorithms are beneficial to the active safety improvement of IEVs

Localization and Mapping for Self-Driving Vehicles:A Survey

The upsurge of autonomous vehicles in the automobile industry will lead to better driving experiences while also enabling the users to solve challenging navigation problems. Reaching such capabilities will require significant technological attention and the flawless execution of various complex tasks, one of which is ensuring robust localization and mapping. Recent surveys have not provided a meaningful and comprehensive description of the current approaches in this field. Accordingly, this review is intended to provide adequate coverage of the problems affecting autonomous vehicles in this area, by examining the most recent methods for mapping and localization as well as related feature extraction and data security problems. First, a discussion of the contemporary methods of extracting relevant features from equipped sensors and their categorization as semantic, non-semantic, and deep learning methods is presented. We conclude that representativeness, low cost, and accessibility are crucial constraints in the choice of the methods to be adopted for localization and mapping tasks. Second, the survey focuses on methods to build a vehicle’s environment map, considering both the commercial and the academic solutions available. The analysis proposes a difference between two types of environment, known and unknown, and develops solutions in each case. Third, the survey explores different approaches to vehicles’ localization and also classifies them according to their mathematical characteristics and priorities. Each section concludes by presenting the related challenges and some future directions. The article also highlights the security problems likely to be encountered in self-driving vehicles, with an assessment of possible defense mechanisms that could prevent security attacks in vehicles. Finally, the article ends with a debate on the potential impacts of autonomous driving, spanning energy consumption and emission reduction, sound and light pollution, integration into smart cities, infrastructure optimization, and software refinement. This thorough investigation aims to foster a comprehensive understanding of the diverse implications of autonomous driving across various domains

Vision-Based Over-Height Vehicle Detection for Warning Drivers

Many older bridges and tunnels were constructed using standards by now many decades out-of-date, at a time when trucks and other large vehicles were smaller. A bridge or tunnel strike is an incidence in which a vehicle, typically a lorry (truck) or double-decker bus, tries to pass under a bridge or tunnel that is lower than its height, subsequently colliding with the structure. These strikes lead to an increased cost of bridge repairs, clogged up roadways and increased potential for catastrophic events: hazardous spillage and/or total collapse. Today, Network Rail reports on average a strike every 4.5 hours. There are a number of reasons why strikes occur, and why drivers of heavy goods vehicles sometimes fail to recognise the warning signs, consequently striking the bridge or tunnel. At first glance, it may seem like the problem is a fairly easy one to solve; however, no matter how well planned the road system, human error is an ever-present risk. The research proposes to address the problem of bridge and tunnel strike prevention and management. The intent of the research is to develop an affordable, reliable and robust early warning over-height detection system bridge-owners can implement at locations with high strike occurrences. The research aims to test and validate a novel vision-based system using a single camera to accurately detect over-height vehicles using a set of optimised parameters. The system uses a camera installed at the offending height, which acts as an “over-height plane” formed by the averages of the maximum allowable heights across all lanes in a given traffic direction. Any vehicle exceeding this plane is analysed within a region of interest using a trigger-based approach for accurate detection and driver warning. If the vehicle is deemed to be over-height, a warning is issued to the driver. As a result, prolonging life expectancy of structures while decreasing the cost of repairs, maintenance and inspections.Transport for London Cambridge Centre for Smart Infrastructure Cambridge Overseas Trust Marie Curie Staff Exchang