To facilitate such a system design, several key challenges must be addressed: The basic idea is to use the phone’s gyroscope data to capture differences in the vehicle’s angular speed due to vehicle steering maneuvers, including turns, lane-changes and U-turns. In this paper, we make use of the inertial sensors existing on smartphones to take one step forward by developing a real-time accurate recognition system for dangerous vehicle steering maneuvers without the driver’s involvement. Consequently, they are key tools to develop ubiquitous mobile solutions. This makes them suitable enablers to capture a wide range of contextual features, like weather and traffic conditions, or user behaviors. In the mobile computing era, smartphones have become instrumental tools to develop innovative mobile context-aware systems because of their numerous sensors such as GPS, accelerometers, gyroscopes. However, these methods are subject to the influence of environmental elements or the high costs of hardware so these safety solutions cannot be applied to a wide range of type/year car models. They all exploit the advanced built-in sensors (e.g., cameras, radars, and infrared sensors) to detect the lane for driving assistance. Steering-assistance systems, such as lane-departure warning or lane-keeping assistance, are typical examples. Therefore, an important means to reduce the probability of traffic accidents is by reminding the drivers that dangerous vehicle maneuvers maybe exist around the road, thereby enhancing the driver’s safety awareness. These maneuvers seriously endanger both the driver and public transport. These actions include erratic lane-changes, making improper turns and excessive speeding. In a study made by the American Automobile Association (AAA) Foundation for Traffic Safety, as many as 56% of deadly crashes involved one or more vehicle maneuvers typically associated with aggressive driving. The factors causing traffic accidents are many, among which human factors are dominant. The results of extensive experiments show that the average accuracy rate of the presented recognition reaches 95%, which implies that the proposed smartphone-based method is suitable for recognizing dangerous vehicle steering maneuvers. Three steering maneuvers including turns, lane-changes and U-turns are defined, and a vehicle angular velocity matching algorithm based on Fast Dynamic Time Warping (FastDTW) is adopted to recognize the vehicle steering.

To identify vehicle steering maneuvers, we focus on the vehicle’s angular velocity, which is characterized by gyroscope data from a smartphone mounted in the vehicle. In this paper, we present a recognition system for dangerous vehicle steering based on the low-cost sensors found in a smartphone: i.e., the gyroscope and the accelerometer. In the mobile computing era, smartphones have become key tools to develop innovative mobile context-aware systems. Existing solutions using video or in-vehicle sensors have been employed to identify dangerous vehicle maneuvers, but these methods are subject to the effects of the environmental elements or the hardware is very costly. Recognizing how a vehicle is steered and then alerting drivers in real time is of utmost importance to the vehicle and driver’s safety, since fatal accidents are often caused by dangerous vehicle maneuvers, such as rapid turns, fast lane-changes, etc.

Sims 3 vehicle speed list