Component faults, bugs, and malicious attacks can all degrade in, or even prevent Semi-Autonomous Vehicles (SAVs) from, correctly capturing their operation context, which is essential to support critical safety features like emergency braking or wheel-steering. While safety features in contemporary SAVs usually rely on static assignment of control priority, such a design may lead to catastrophic accidents when accompanied with erroneous/compromised control and context estimation. To mitigate the grave consequence of using incorrect data, we propose CADCA , a novel control decision-maker for SAVs, that is designed to operate under sensor/data errors or falsifications as well as malicious/erroneous control inputs with the ultimate goal of resolving conflicting control inputs to ensure safety. Our evaluation of \( > \) 15,700 test-cases has shown CADCA to achieve a 98% success rate in preventing the execution of incorrect control decisions caused by component failures and/or malicious attacks in the most common (i.e., rear-end) collisions.

Replacing traditional vehicular components with electronic components brings numerous benefits but also introduces new vulnerabilities. To cope with this double-edged trend, we propose Context-Aware Detection of abnormal vehicle Dynamics (CADD) in general, or abnormal vehicle accelerations in particular. To account for the limited data availability common in production vehicles, we propose a new detection mechanism based on estimated vehicular contexts, instead of the commonly used “predict-input-then-compare.” That is, without relying on the unrealistically assumed availability of detailed measurements for accurate behavior modeling and prediction, CADD utilizes four sets of vehicle data to perform anomaly detection by cross-validating estimations of the underlying driving contexts, including road inclination, tire slippage, and total mass. Our extensive evaluation with > 87,000 test-cases has shown CADD to achieve > 96% recall and < 0.5% false positive rate. Furthermore, CADD can efficiently pinpoint the anomalous group of data with > 95% accuracy when the vehicle’s behavior deviates 0.07g (0.69 m/s2) from its normal pattern.

A new in-vehicle phone localization scheme, calledDAPL(Detection andAlarming of in-vehiclePhoneLocation), is proposed to determine the locations of smartphones inside a moving car, with the goal of preventing smartphone-distracted driving.DAPLoperates on commodity smartphones and cars, and does not require additional special/dedicated hardware to be installed inside the car, making its deployment easy and attractive to users and carmakers. Even when a phone is moved from one location to another inside a moving car,DAPLwill detect this movement, acquire the sensor data, and estimate the phone's destination location.DAPLcaptures the trajectory of each phone movement, the change of magnetic field, and the RSSI readings from the Bluetooth transceiver built in most cars, and then estimates the phone's destination location by matching the trajectory with the variation of magnetic field and the Bluetooth RSSI readings. Our extensive experimentation has shownDAPLto achieve an average of 91.71% accuracy of in-car phone localization at low energy overhead, i.e.,$<$2.5% reduction of actual usage (screen-on) time.

People use their mobile devices anywhere and anytime to run various apps, and the information shown on their device screens can be seen by nearby (unauthorized) parties, called shoulder surfers. To mitigate this privacy threat, we have developed HideScreen by utilizing the human vision and optical system properties to hide the users' on-screen information from the shoulder surfers.

The information displayed on mobile device screens can be seen by nearby (unauthorized) parties, called shoulder surfers. To protect sensitive on-screen information, we have developed HideScreen by utilizing the human vision and optical system properties to hide the users' on-screen information from the shoulder surfers.