Related papers: Minimizing Sensor Allocation Cost for Crowdsensing…
Drive-by sensing is a promising data collection paradigm that leverages the mobilities of vehicles to survey urban environments at low costs, contributing to the positive externality of urban transport activities. Focusing on e-hailing…
Lane detection is very important for self-driving vehicles. In recent years, computer stereo vision has been prevalently used to enhance the accuracy of the lane detection systems. This paper mainly presents a multiple lane detection…
On-street parking is convenient, but has many disadvantages: on-street spots come at the expense of other road uses such as traffic lanes, transit lanes, bike lanes, or parklets; drivers looking for parking contribute substantially to…
The sensing and monitoring of the urban road network contribute to the efficient operation of the urban transportation system and the functionality of urban systems. However, traditional sensing methods, such as inductive loop sensors,…
Vehicle-sharing systems are becoming important for urban transportation. In these systems, users arrive at a station, pick up a vehicle, use it for a while and then return it to another station of their choice. Depending on the type of…
Countries with access to large bodies of water often aim to protect their maritime transport by employing maritime surveillance systems. However, the number of available sensors (e.g., cameras) is typically small compared to the…
The development of self-driving cars has garnered significant attention from researchers, universities, and industries worldwide. Autonomous vehicles integrate numerous subsystems, including lane tracking, object detection, and vehicle…
Monitoring vehicle flows in cities is crucial to improve the urban environment and quality of life of citizens. Images are the best sensing modality to perceive and assess the flow of vehicles in large areas. Current technologies for…
Parking spaces are costly to build, parking payments are difficult to enforce, and drivers waste an excessive amount of time searching for empty lots. Accurate quantification would inform developers and municipalities in space allocation…
In this paper, a deep learning approach is presented for direction of arrival estimation using automotive-grade ultrasonic sensors which are used for driving assistance systems such as automatic parking. A study and implementation of the…
This work proposes a centrality-based approach to identify data offloading points in a VSN. The solution presents a scheme to select vehicles used as aggregation points to collect and aggregate other vehicles' data before uploading it to…
Intelligent Transportation Systems (ITSs) providing vehicle-related statistical data are one of the key components for future smart cities. In this context, knowledge about the current traffic flow is used for travel time reduction and…
Anomaly detection from a driver's perspective when driving is important to autonomous vehicles. As a part of Advanced Driver Assistance Systems (ADAS), it can remind the driver about dangers timely. Compared with traditional studied scenes…
The problem of optimally placing sensors under a cost constraint arises naturally in the design of industrial and commercial products, as well as in scientific experiments. We consider a relaxation of the full optimization formulation of…
Selecting an appropriate clustering method as well as an optimal number of clusters in road accident data is at times confusing and difficult. This paper analyzes shortcomings of different existing techniques applied to cluster…
Public charging infrastructure plays a crucial role in the context of electrifying the private mobility sector in particular for urban regions. Against this background, we develop a new mathematical model for the optimal placement of public…
Robust localization in dense urban scenarios using a low-cost sensor setup and sparse HD maps is highly relevant for the current advances in autonomous driving, but remains a challenging topic in research. We present a novel monocular…
The aim of crowd counting is to estimate the number of people in images by leveraging the annotation of center positions for pedestrians' heads. Promising progresses have been made with the prevalence of deep Convolutional Neural Networks.…
We study a real-time smart charging algorithm for electric vehicles (EVs) at a workplace parking lot in order to minimize electricity cost from time-of-use electricity rates and demand charges while ensuring that the owners of the EVs…
We propose a novel and pragmatic framework for traffic scene perception with roadside cameras. The proposed framework covers a full-stack of roadside perception pipeline for infrastructure-assisted autonomous driving, including object…