Integrated self-consistent macro-micro traffic flow modeling and calibration framework based on trajectory data

Calibrating microscopic car-following (CF) models is crucial in traffic flow theory as it allows for accurate reproduction and investigation of traffic behavior and phenomena. Typically, the calibration procedure is a complicated, non-convex optimization issue. When the traffic state is in equilibrium, the macroscopic flow model can be derived analytically from the corresponding CF model. In contrast to the microscopic CF model, calibrated based on trajectory data, the macroscopic representation of the fundamental diagram (FD) primarily adopts loop detector data for calibration. The different calibration approaches at the macro- and microscopic levels may lead to misaligned parameters with identical practical meanings in both macro- and micro-traffic models. This inconsistency arises from the difference between the parameter calibration processes used in macro- and microscopic traffic flow models. Hence, this study proposes an integrated multiresolution traffic flow modeling framework using the same trajectory data for parameter calibration based on the self-consistency concept. This framework incorporates multiple objective functions in the macro- and micro-dimensions. To expeditiously execute the proposed framework, an improved metaheuristic multi-objective optimization algorithm is presented that employs multiple enhancement strategies. Additionally, a deep learning technique based on attention mechanisms was used to extract stationary-state traffic data for the macroscopic calibration process, instead of directly using the entire aggregated data. We conducted experiments using real-world and synthetic trajectory data to validate our self-consistent calibration framework

Attention and Anticipation in Fast Visual-Inertial Navigation

We study a Visual-Inertial Navigation (VIN) problem in which a robot needs to estimate its state using an on-board camera and an inertial sensor, without any prior knowledge of the external environment. We consider the case in which the robot can allocate limited resources to VIN, due to tight computational constraints. Therefore, we answer the following question: under limited resources, what are the most relevant visual cues to maximize the performance of visual-inertial navigation? Our approach has four key ingredients. First, it is task-driven, in that the selection of the visual cues is guided by a metric quantifying the VIN performance. Second, it exploits the notion of anticipation, since it uses a simplified model for forward-simulation of robot dynamics, predicting the utility of a set of visual cues over a future time horizon. Third, it is efficient and easy to implement, since it leads to a greedy algorithm for the selection of the most relevant visual cues. Fourth, it provides formal performance guarantees: we leverage submodularity to prove that the greedy selection cannot be far from the optimal (combinatorial) selection. Simulations and real experiments on agile drones show that our approach ensures state-of-the-art VIN performance while maintaining a lean processing time. In the easy scenarios, our approach outperforms appearance-based feature selection in terms of localization errors. In the most challenging scenarios, it enables accurate visual-inertial navigation while appearance-based feature selection fails to track robot's motion during aggressive maneuvers.Comment: 20 pages, 7 figures, 2 table

A New Car-Following Model with Incorporation of Markkula’s Framework of Sensorimotor Control in Sustained Motion Tasks

The main aim of this research is to develop a new car-following model that realistically predicts the trajectories of speed, acceleration, jerk and spacing in congested and uncongested freeway conditions. The research has three objectives. First, the start time of driver reaction was investigated in various car-following conditions. Specifically, the assumption of a constant reaction time of the existing car-following models was investigated using the observed driver behaviour data collected from a driving simulator. Moreover, the perception limits and the process that drivers use to start reaction were also studied. Second, a new car-following model was developed to reproduce the observed driver’s intermittent start time of acceleration/deceleration and realistic ranges of magnitudes of speed, spacing, acceleration and jerk. For this task, the model adapted the Markkula’s Framework of Sensorimotor Control in Sustained Motion Tasks. Third, the effect of lead vehicle type (car and truck) and the effect of the lead vehicle brake lights on the start time of driver reaction in car-following conditions were studied. For this purpose, a total of 50 drivers’ car-following behaviour was observed in 4 different scenarios using a driving simulator – reaction to a decelerating lead vehicle, reaction to a stopped lead vehicle, perception of a lead vehicle’s speed change, and perception of a slow-moving lead vehicle. It was found that the drivers neither reacted after a specific reaction time from the start of perception nor reacted at a specific value of a perceptual variable. Rather, the drivers generally reacted when the accumulation of evidence (e.g., perceptual variable) over time reached a threshold. This demonstrates that the evidence accumulation framework was a promising method of predicting the start time of driver reaction in car-following conditions. Therefore, a new car-following model called “Intermittent Intelligent Driver Model (IIDM)” was developed based on evidence accumulation to start driver reaction unlike the existing car-following models that use a constant reaction time parameter. Moreover, the IIDM uses the shape and duration of acceleration adjustments that accurately represents the actual shape and duration of acceleration maneuvers in the data. The prediction of accuracy of the new car-following model was evaluated using both the driving simulator data and real-world trajectory data. Compared to the three existing car-following models – the Gipps’ Model, the Wiedemann Model and the Intelligent Driver Model (IDM), the IIDM realistically reproduced trajectories of speed, acceleration, jerk and spacing for both types of data. Moreover, the estimated surrogate measures of safety from trajectories predicted using the IIDM were similar to the surrogate measures of safety estimated from the observed data. Furthermore, the IIDM can incorporate the effects of lead vehicle brake lights and lead vehicle type (car and truck) for more accurate estimation of the driver reaction time. This demonstrates that the IIDM can generate more realistic vehicle trajectories (start time of reaction and magnitude of reaction) in various car-following conditions, which can be used to predict vehicle speeds and assess safety

Reliable and safe autonomy for ground vehicles in unstructured environments

This thesis is concerned with the algorithms and systems that are required to enable safe autonomous operation of an unmanned ground vehicle (UGV) in an unstructured and unknown environment; one in which there is no speci c infrastructure to assist the vehicle autonomy and complete a priori information is not available. Under these conditions it is necessary for an autonomous system to perceive the surrounding environment, in order to perform safe and reliable control actions with respect to the context of the vehicle, its task and the world. Speci cally, exteroceptive sensors measure physical properties of the world. This information is interpreted to extract a higher level perception, then mapped to provide a consistent spatial context. This map of perceived information forms an integral part of the autonomous UGV (AUGV) control system architecture, therefore any perception or mapping errors reduce the reliability and safety of the system. Currently, commercially viable autonomous systems achieve the requisite level of reliability and safety by using strong structure within their operational environment. This permits the use of powerful assumptions about the world, which greatly simplify the perception requirements. For example, in an urban context, things that look approximately like roads are roads. In an indoor environment, vertical structure must be avoided and everything else is traversable. By contrast, when this structure is not available, little can be assumed and the burden on perception is very large. In these cases, reliability and safety must currently be provided by a tightly integrated human supervisor. The major contribution of this thesis is to provide a holistic approach to identify and mitigate the primary sources of error in typical AUGV sensor feedback systems (comprising perception and mapping), to promote reliability and safety. This includes an analysis of the geometric and temporal errors that occur in the coordinate transformations that are required for mapping and methods to minimise these errors in real systems. Interpretive errors are also studied and methods to mitigate them are presented. These methods combine information theoretic measures with multiple sensor modalities, to improve perceptive classi cation and provide sensor redundancy. The work in this thesis is implemented and tested on a real AUGV system, but the methods do not rely on any particular aspects of this vehicle. They are all generally and widely applicable. This thesis provides a rm base at a low level, from which continued research in autonomous reliability and safety at ever higher levels can be performed

Analysis of air quality management with emphasis on transportation sources

The current environment and practices of air quality management were examined for three regions: Denver, Phoenix, and the South Coast Air Basin of California. These regions were chosen because the majority of their air pollution emissions are related to mobile sources. The impact of auto exhaust on the air quality management process is characterized and assessed. An examination of the uncertainties in air pollutant measurements, emission inventories, meteorological parameters, atmospheric chemistry, and air quality simulation models is performed. The implications of these uncertainties to current air quality management practices is discussed. A set of corrective actions are recommended to reduce these uncertainties

Evaluating the accuracy of vehicle tracking data obtained from Unmanned Aerial Vehicles

Abstract This paper presents a methodology for tracking moving vehicles that integrates Unmanned Aerial Vehicles with video processing techniques. The authors investigated the usefulness of Unmanned Aerial Vehicles to capture reliable individual vehicle data by using GPS technology as a benchmark. A video processing algorithm for vehicles trajectory acquisition is introduced. The algorithm is based on OpenCV libraries. In order to assess the accuracy of the proposed video processing algorithm an instrumented vehicle was equipped with a high precision GPS. The video capture experiments were performed in two case studies. From the field, about 24,000 positioning data were acquired for the analysis. The results of these experiments highlight the versatility of the Unmanned Aerial Vehicles technology combined with video processing technique in monitoring real traffic data