Abstract: Left turns are known to be one of the most dangerous driving maneuvers. An effective way to mitigate this safety risk is to install a left-turn enforcement—for example, a protected left-turn signal or all-way stop signs—at every turn that preserves a traffic phase exclusively for left turns. Although this protection scheme can significantly increase the driving safety, information on whether or not a road segment (e.g., intersection) has such a setting is not yet available to the public and navigation systems. This disclosure presents a system that exploits mobile crowdsensing and deep learning to classify the protection settings of left turns.

Abstract: An object tracking method, device, backend, and medium. The object tracking method includes: obtaining raw sensor data sent by a sensor array, the sensor array including a plurality of magnetic field sensors, the raw sensor data including magnetic field strengths at locations of the magnetic field sensors; determining the presence of a target magnet based on the raw sensor data, where the target magnet is a permanent magnet provided on a target object; when the target magnet is present, tracking the target magnet based on the raw sensor data. The method enables permanent-magnet-based object tracking.

Abstract: Driver fingerprinting using sensor data was known to be feasible only with access to in-car data. This disclosure presents a novel technique for identifying a vehicle driver from only one vehicle turn and using zero-permission sensors residing in the mobile device. Through extensive evaluations, extracted features are shown to reflect only the drivers unique turning style and thus functions as the core of driver fingerprinting.

Abstract: Left turns are known to be one of the most dangerous driving maneuvers. An effective way to mitigate this safety risk is to install a left-turn enforcement—for example, a protected left-turn signal or all-way stop signs—at every turn that preserves a traffic phase exclusively for left turns. Although this protection scheme can significantly increase the driving safety, information on whether or not a road segment (e.g., intersection) has such a setting is not yet available to the public and navigation systems. This disclosure presents a system that exploits mobile crowdsensing and deep learning to classify the protection settings of left turns.

Abstract: Examples disclosed herein involve determining a location of a Bluetooth device relative to a detecting device based on measured received signal strength indication (RSSI). In examples herein, a regression analysis of RSSIs of a Bluetooth signal and locations of a detecting device are used to determine the location of the target Bluetooth device.

Abstract: Driver fingerprinting using sensor data was known to be feasible only with access to in-car data. This disclosure presents a novel technique for identifying a vehicle driver from only one vehicle turn and using zero-permission sensors residing in the mobile device. Through extensive evaluations, extracted features are shown to reflect only the drivers unique turning style and thus functions as the core of driver fingerprinting.

Abstract: Examples disclosed herein involve determining a location of a Bluetooth device relative to a detecting device based on measured received signal strength indication (RSSI). In examples herein, a regression analysis of RSSIs of a Bluetooth signal and locations of a detecting device are used to determine the location of the target Bluetooth device.

Abstract: A vehicle maneuver detection application is proposed for driving assistant systems. The application can accurately and inexpensively detect and differentiate vehicle steering maneuvers by utilizing built-in sensors on smartphones or other portable computing device residing in a vehicle. By leveraging an effective bump detection algorithm and studying the nature of steering, the application is capable of differentiating various steering patterns, such as lane change, turn, and driving on curvy roads. Practicality of the application is demonstrates by two use cases: careless steering detection and fine-grained lane guidance. Thus, the application provides new functionalities without relying on cameras to provide a broader range of driving assistance.

Abstract: A vehicle maneuver detection application is proposed for driving assistant systems. The application can accurately and inexpensively detect and differentiate vehicle steering maneuvers by utilizing built-in sensors on smartphones or other portable computing device residing in a vehicle. By leveraging an effective bump detection algorithm and studying the nature of steering, the application is capable of differentiating various steering patterns, such as lane change, turn, and driving on curvy roads. Practicality of the application is demonstrates by two use cases: careless steering detection and fine-grained lane guidance. Thus, the application provides new functionalities without relying on cameras to provide a broader range of driving assistance.