Abstract:The environmental impact of connected and autonomous vehicles (CAVs) is still uncertain. Little is known about how CAVs operational behavior influences the environmental performance of network traffic, including conventional vehicles (CVs). In this paper, a microscopic traffic and emission modeling platform was applied to simulate CAVs operation in Motorway, Rural, and Urban road sections of a medium-sized European city, assuming different configurations of the car-following model parameters associated with a … Show more
“…To solve the camera calibration problem, using the checkerboard as a calibration reference, it is necessary to estimate the pose C B T of the checkerboard in the camera coordinate system. First, detect the corner points of the checkerboard, and then obtain the corresponding solution by solving the PnP (Perspective-n-Point) problem, see (1).…”
Section: B Extrinsic Calibration Methods Based On Auxiliary Camera Fo...mentioning
confidence: 99%
“…In recent years, with the steady and rapid development of the economy and the continuous improvement of the level of science and technology, the number of private cars is increasing. While these changes have brought convenience to people's life and travel, they have also brought about a series of social problems such as traffic congestion, serious environmental pollution [1], and intense energy and resource consumption. Throughout the driver's journey, problems such as traffic congestion, frequent accidents, and vehicle failures emerge in an endless stream.…”
Multiple infrastructure RGB-D cameras can be used for localizing autonomous vehicles in Automated Valet Parking. The accurate calibration of these cameras' extrinsic parameters is crucial. However, due to the sparse and distributed placement of the cameras, the field of view (FOV) between them is very small. This makes the calibration process complex and dependent on human expertise. To address this, this paper proposes an automatic extrinsic calibration method for multiple infrastructure cameras with a small FOV. The method introduces an auxiliary camera to enhance the association between the multiple infrastructure cameras. A moving checkerboard placed within the public FOV is utilized as a reference for calibration. The optimization method involves constructing a pose graph to store the poses of the cameras and checkerboard, and it solves the pose graph by calculating the reprojection errors of the checkerboard. The experimental results demonstrate that the proposed method achieves a calibration accuracy of two centimeters. It outperforms other calibration methods when applied to a constructed multiple RGB-D camera system. Furthermore, the proposed method is simple and efficient in the real calibration procedure.
“…To solve the camera calibration problem, using the checkerboard as a calibration reference, it is necessary to estimate the pose C B T of the checkerboard in the camera coordinate system. First, detect the corner points of the checkerboard, and then obtain the corresponding solution by solving the PnP (Perspective-n-Point) problem, see (1).…”
Section: B Extrinsic Calibration Methods Based On Auxiliary Camera Fo...mentioning
confidence: 99%
“…In recent years, with the steady and rapid development of the economy and the continuous improvement of the level of science and technology, the number of private cars is increasing. While these changes have brought convenience to people's life and travel, they have also brought about a series of social problems such as traffic congestion, serious environmental pollution [1], and intense energy and resource consumption. Throughout the driver's journey, problems such as traffic congestion, frequent accidents, and vehicle failures emerge in an endless stream.…”
Multiple infrastructure RGB-D cameras can be used for localizing autonomous vehicles in Automated Valet Parking. The accurate calibration of these cameras' extrinsic parameters is crucial. However, due to the sparse and distributed placement of the cameras, the field of view (FOV) between them is very small. This makes the calibration process complex and dependent on human expertise. To address this, this paper proposes an automatic extrinsic calibration method for multiple infrastructure cameras with a small FOV. The method introduces an auxiliary camera to enhance the association between the multiple infrastructure cameras. A moving checkerboard placed within the public FOV is utilized as a reference for calibration. The optimization method involves constructing a pose graph to store the poses of the cameras and checkerboard, and it solves the pose graph by calculating the reprojection errors of the checkerboard. The experimental results demonstrate that the proposed method achieves a calibration accuracy of two centimeters. It outperforms other calibration methods when applied to a constructed multiple RGB-D camera system. Furthermore, the proposed method is simple and efficient in the real calibration procedure.
“…• Reduced air pollution: In the higher levels of automation, particularly levels 4 and 5, driving will be smoother and more efficient so that fuel consumption and carbon dioxide emissions will be reduced remarkably [59]. As well as, by minimizing waiting times and improving traffic flow, the release of polluting gases caused by traffic congestion and frequent stopping and starting of vehicles will be considerably reduced [60], [61].…”
In Intelligent Transportation Systems (ITS), ensuring road safety has paved the way for innovative advancements such as autonomous driving. These self-driving vehicles, with their variety of sensors, harness the potential to minimize human driving errors and enhance transportation efficiency via sophisticated AI modules. However, the reliability of these sensors remains challenging, especially as they can be vulnerable to anomalies resulting from adverse weather, technical issues, and cyber-attacks. Such inconsistencies can lead to imprecise or erroneous navigation decisions for autonomous vehicles that can result in fatal consequences, e.g., failure in recognizing obstacles. This survey delivers a comprehensive review of the latest research on solutions for detecting anomalies in sensor data. After laying the foundation on the workings of the connected and autonomous vehicles, we categorize anomaly detection methods into three groups: statistical, classical machine learning, and deep learning techniques. We provide a qualitative assessment of these methods to underline existing research limitations. We conclude by spotlighting key research questions to enhance the dependability of autonomous driving in forthcoming studies.
“…In particular, the dynamic description of the phenomena involved will be enriched. For instance, it could be appropriate to include the dynamics of the state of charge of the batteries of the CAVs [53], investigate the use of different fuel consumption models (e.g., [54], [55]), and add the description of the pollutant emissions of the traditional vehicles that make up the macroscopic traffic (see for instance [56] and [57]).…”
In this paper a novel hierarchical multi-level control scheme is proposed for freeway traffic systems. Relying on a coupled PDE-ODE nominal model, capturing the interaction between the macroscopic traffic flow and a platoon of connected and automated electric vehicles (CAVs) which acts as a moving bottleneck, a high-level model predictive controller (MPC) is adopted to reduce traffic congestion and vehicle fuel consumption. This controller generates, only when necessary, i.e., according to an eventtriggered control logic, the most appropriate reference values for the platoon length and velocity. The platoon is in turn controlled, in an energy efficient way, by a distributed medium-level MPC, so as to track the reference speed values for its downstream and upstream end-points provided by the high-level MPC. The mismatch between the dynamics of the CAVs forming the platoon and their nominal dynamics is tackled via the design of local low-level robust integral sliding mode controllers, which have the capability of compensating for the mismatch. In the paper, the controlled platoon of CAVs is assumed to be immersed into a realistic traffic system with traffic demand not known in advance, which differs from the nominal prediction model used by the high-level MPC.INDEX TERMS Electric vehicles, event-triggered control, optimal control, platoon control, sliding mode control, traffic control.
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