Dashboard camera installations are becoming increasingly common due to various Advanced Driver Assistance Systems (ADAS) based services provided by them. Though deployed primarily for crash recordings, calibrating these cameras can allow them to measure real-world distances, which can enable a broad spectrum of ADAS applications such as lane-detection, safe driving distance estimation, collision prediction, and collision prevention. Today, dashboard camera calibration is a tedious manual process that requires a trained professional who needs to use a known pattern (e.g., chessboard-like) at a calibrated distance. In this paper, we propose SmartDashCam, a system for automatic and live calibration of dashboard cameras which always ensures highly accurate calibration values. Smart-DashCam leverages collecting images of a large number of vehicles appearing in front of the camera and using their coarse geometric shapes to derive the calibration parameters. In sharp contrast to the manual process we are proposing the use of a large amount of data and machine learning techniques to arrive at calibration accuracies that are comparable to the manual process. SmartDashCam implemented using commodity dashboard cameras estimates real-world distances with mean errors of 5.7% which closely rivals the 4.1% mean error obtained from traditional manual calibration using known patterns.

Emerging smart cities are typically equipped with thousands of outdoor cameras. However, these cameras are usually not calibrated, i.e., information such as their precise mounting height and orientation is not available. Calibrating these cameras allows measurement of real-world distances from the video, thereby enabling a wide range of novel applications such as identifying speeding vehicles and city road planning . Unfortunately, robust camera calibration is a manual process today and is not scalable. In this article, we propose AutoCalib, a system for scalable, automatic calibration of traffic cameras. AutoCalib exploits deep learning to extract selected key-point features from car images in the video and uses a novel filtering and aggregation algorithm to automatically produce a robust estimate of the camera calibration parameters from just hundreds of samples. We have implemented AutoCalib as a service on Azure that takes in a video segment and computes the camera calibration parameters. Using video from real-world traffic cameras, we show that AutoCalib is able to estimate real-world distances with an error of less than 12%.

Inter-vehicular communication (IVC) can be explored to build a live map of vehicles for enhancing collaborative vehicular applications related to traffic statistics, safety by accident prediction and prevention, and energy efficient route planning. For enhancing the abovementioned applications, a live map of vehicles associated with their communication identities (e.g., IP/MAC addresses) of other collaborating vehicles is needed. This is particularly challenging to achieve in the presence of legacy vehicles which may not have any sensing or IVC capabilities. Additionally, vehicles might have diverse sensing capabilities and can have contradicting opinions of surrounding vehicles. We present RoadView, a system that builds the live map of surrounding vehicles by intelligently fusing the local maps created by individual vehicles. RoadView runs on top of existing local vehicular matching systems (LM) such as Foresight [10] or RoadMap [20]. RoadView is the first work that provides a live map of vehicles by leveraging collaboration across vehicles. Our simulations show that for different adoption rates and traffic densities, RoadView can robustly fuse information from a collection of local maps and enhance vehicles to sense 1.8x (average) number of immediate neighboring vehicles compared to state of art LM algorithms.