Biblio
With self-driving cars making their way on to our roads, we ask not what it would take for them to gain acceptance among consumers, but what impact they may have on other drivers. How they will be perceived and whether they will be trusted will likely have a major effect on traffic flow and vehicular safety. This work first undertakes an exploratory factor analysis to validate a trust scale for human-robot interaction and shows how previously validated metrics and general trust theory support a more complete model of trust that has increased applicability in the driving domain. We experimentally test this expanded model in the context of human-automation interaction during simulated driving, revealing how using these dimensions uncovers significant biases within human-robot trust that may have particularly deleterious effects when it comes to sharing our future roads with automated vehicles.
The incidence of abnormal road traffic events, especially abnormal traffic congestion, is becoming more and more prominent in daily traffic management in China. It has become the main research work of urban traffic management to detect and identify traffic congestion incidents in time. Efficient and accurate detection of traffic congestion incidents can provide a good strategy for traffic management. At present, the detection and recognition of traffic congestion events mainly rely on the integration of road traffic flow data and the passing data collected by electronic police or devices of checkpoint, and then estimating and forecasting road conditions through the method of big data analysis; Such methods often have some disadvantages such as low time-effect, low precision and small prediction range. Therefore, with the help of the current large and medium cities in the public security, traffic police have built video surveillance equipment, through computer vision technology to analyze the traffic flow from video monitoring, in this paper, the motion state and the changing trend of vehicle flow are obtained by using the technology of vehicle detection from video and multi-target tracking based on deep learning, so as to realize the perception and recognition of traffic congestion. The method achieves the recognition accuracy of less than 60 seconds in real-time, more than 80% in detection rate of congestion event and more than 82.5% in accuracy of detection. At the same time, it breaks through the restriction of traditional big data prediction, such as traffic flow data, truck pass data and GPS floating car data, and enlarges the scene and scope of detection.
Deep learning has undergone tremendous advancements in computer vision studies. The training of deep learning neural networks depends on a considerable amount of ground truth datasets. However, labeling ground truth data is a labor-intensive task, particularly for large-volume video analytics applications such as video surveillance and vehicles detection for autonomous driving. This paper presents a rapid and accurate method for associative searching in big image data obtained from security monitoring systems. We developed a semi-automatic moving object annotation method for improving deep learning models. The proposed method comprises three stages, namely automatic foreground object extraction, object annotation in subsequent video frames, and dataset construction using human-in-the-loop quick selection. Furthermore, the proposed method expedites dataset collection and ground truth annotation processes. In contrast to data augmentation and data generative models, the proposed method produces a large amount of real data, which may facilitate training results and avoid adverse effects engendered by artifactual data. We applied the constructed annotation dataset to train a deep learning you-only-look-once (YOLO) model to perform vehicle detection on street intersection surveillance videos. Experimental results demonstrated that the accurate detection performance was improved from a mean average precision (mAP) of 83.99 to 88.03.
In this paper we consider connected and autonomous vehicles (CAV) in a traffic network as moving waves defined by their frequency and phase. This outlook allows us to develop a multi-layer decentralized control strategy that achieves the following desirable behaviors: (1) safe spacing between vehicles traveling down the same road, (2) coordinated safe crossing at intersections of conflicting flows, (3) smooth velocity profiles when traversing adjacent intersections. The approach consist of using the Kuramoto equation to synchronize the phase and frequency of agents in the network. The output of this layer serves as the reference trajectory for a back-stepping controller that interfaces the first-order dynamics of the phase-domain layer and the second order dynamics of the vehicle. We show the performance of the strategy for a single intersection and a small urban grid network. The literature has focused on solving the intersection coordination problem in both a centralized and decentralized manner. Some authors have even used the Kuramoto equation to achieve synchronization of traffic lights. Our proposed strategy falls in the rubric of a decentralized approach, but unlike previous work, it defines the vehicles as the oscillating agents, and leverages their inter-connectivity to achieve network-wide synchronization. In this way, it combines the benefits of coordinating the crossing of vehicles at individual intersections and synchronizing flow from adjacent junctions.
Cooperative Intelligent Transport Systems (C-ITS) are expected to play an important role in our lives. They will improve the traffic safety and bring about a revolution on the driving experience. However, these benefits are counterbalanced by possible attacks that threaten not only the vehicle's security, but also passengers' lives. One of the most common attacks is the Sybil attack, which is even more dangerous than others because it could be the starting point of many other attacks in C-ITS. This paper proposes a distributed approach allowing the detection of Sybil attacks by using the traffic flow theory. The key idea here is that each vehicle will monitor its neighbourhood in order to detect an eventual Sybil attack. This is achieved by a comparison between the real accurate speed of the vehicle and the one estimated using the V2V communications with vehicles in the vicinity. The estimated speed is derived by using the traffic flow fundamental diagram of the road's portion where the vehicles are moving. This detection algorithm is validated through some extensive simulations conducted using the well-known NS3 network simulator with SUMO traffic simulator.
As the traffic congestion increases on the transport network, Payable on the road to slower speeds, longer falter times, as a consequence bigger vehicular queuing, it's necessary to introduce smart way to reduce traffic. We are already edging closer to ``smart city-smart travel''. Today, a large number of smart phone applications and connected sat-naves will help get you to your destination in the quickest and easiest manner possible due to real-time data and communication from a host of sources. In present situation, traffic lights are used in each phase. The other way is to use electronic sensors and magnetic coils that detect the congestion frequency and monitor traffic, but found to be more expensive. Hence we propose a traffic control system using image processing techniques like edge detection. The vehicles will be detected using images instead of sensors. The cameras are installed alongside of the road and it will capture image sequence for every 40 seconds. The digital image processing techniques will be applied to analyse and process the image and according to that the traffic signal lights will be controlled.
In vehicular networks, each message is signed by the generating node to ensure accountability for the contents of that message. For privacy reasons, each vehicle uses a collection of certificates, which for accountability reasons are linked at a central authority. One such design is the Security Credential Management System (SCMS) [1], which is the leading credential management system in the US. The SCMS is composed of multiple components, each of which has a different task for key management, which are logically separated. The SCMS is designed to ensure privacy against a single insider compromise, or against outside adversaries. In this paper, we demonstrate that the current SCMS design fails to achieve its design goal, showing that a compromised authority can gain substantial information about certificate linkages. We propose a solution that accommodates threshold-based detection, but uses relabeling and noise to limit the information that can be learned from a single insider adversary. We also analyze our solution using techniques from differential privacy and validate it using traffic-simulator based experiments. Our results show that our proposed solution prevents privacy information leakage against the compromised authority in collusion with outsider attackers.
In Vehicular networks, privacy, especially the vehicles' location privacy is highly concerned. Several pseudonymous based privacy protection mechanisms have been established and standardized in the past few years by IEEE and ETSI. However, vehicular networks are still vulnerable to Sybil attack. In this paper, a Sybil attack detection method based on k-Nearest Neighbours (kNN) classification algorithm is proposed. In this method, vehicles are classified based on the similarity in their driving patterns. Furthermore, the kNN methods' high runtime complexity issue is also optimized. The simulation results show that our detection method can reach a high detection rate while keeping error rate low.
Software has emerged as a significant part of many domains, including financial service platforms, social networks and vehicle control. Standards organizations have responded to this by creating regulations to address issues such as safety and privacy. In this context, compliance of software with standards has emerged as a key issue. For software development organizations, compliance is a complex and costly goal to achieve and is often accomplished by producing so-called assurance cases, which demonstrate that the system indeed satisfies the property imposed by a standard (e.g., safety, privacy, security). As systems and standards undergo evolution for a variety of reasons, maintaining assurance cases multiplies the effort. In this work, we propose to exploit the connection between the field of model management and the problem of compliance management and propose methods that use model management techniques to address compliance scenarios such as assurance case evolution and reuse. For validation, we ground our approaches on the automotive domain and the ISO 26262 standard for functional safety of road vehicles.
This paper proposes a prototype of a level 3 autonomous vehicle using Raspberry Pi, capable of detecting the nearby vehicles using an IR sensor. We make the first attempt to analyze autonomous vehicles from a microscopic level, focusing on each vehicle and their communications with the nearby vehicles and road-side units. Two sets of passive and active experiments on a pair of prototypes were run, demonstrating the interconnectivity of the developed prototype. Several sensors were incorporated into an emulation based on System-on-Chip to further demonstrate the feasibility of the proposed model.
This paper presents the application of fusion meth- ods to a visual surveillance scenario. The range of relevant features for re-identifying vehicles is discussed, along with the methods for fusing probabilistic estimates derived from these estimates. In particular, two statistical parametric fusion methods are considered: Bayesian Networks and the Dempster Shafer approach. The main contribution of this paper is the development of a metric to allow direct comparison of the benefits of the two methods. This is achieved by generalising the Kelly betting strategy to accommodate a variable total stake for each sample, subject to a fixed expected (mean) stake. This metric provides a method to quantify the extra information provided by the Dempster-Shafer method, in comparison to a Bayesian Fusion approach.