A safety system for a vehicle may include one or more processors configured to determine, based on a friction prediction model, one or more predictive friction coefficients between the ground and one or more tires of the ground vehicle using first ground condition data and second ground condition data. The first ground condition data represent conditions of the ground at or near the position of the ground vehicle, and the second ground condition data represent conditions of the ground in front of the ground vehicle with respect to a driving direction of the ground vehicle. The one or more processors are further configured to determine driving conditions of the ground vehicle using the determined one or more predictive friction coefficients.

A helmet includes a transceiver configured to receive vehicle data from one or more sensors located on a vehicle. The helmet also includes an inertial movement unit (IMU) configured to collect helmet motion data of a rider of the vehicle and a processor in communication with the transceiver and IMU, and programmed to receive, via the transceiver, vehicle data from the one or more sensors located on the vehicle and determine a rider attention state utilizing the vehicle data from the one or more sensors located on the vehicle and the helmet motion data from the IMU.

This application relies on terminology found in the Vehicle State Detection (STATE) patent and describes methods of detecting acceleration, deceleration, accidents and cornering operational states, which we will also call vehicle dependent states, and vehicle independent states, triggered when the portable device is moved independently of movement of the vehicle. These methods enhance classification of driving behavior.

A method for compensating sensor tolerances of accelerometers of a vehicle. The method includes following steps: recording of measurement signals of at least three similarly oriented accelerometers, calculation of an acceleration (a.sub.b,z) at a reference position in the spatial direction, which corresponds to the orientation of the accelerometers, low-pass filtering of the measurement signals, determination of tolerance parameters (c.sub.x, c.sub.y, c.sub.z) of each sensor via an optimization method with the aid of the calculated acceleration (a.sub.b,z) at the reference position, and calculation of the adjusted measurement signals from the recorded measurement signals and the tolerance parameters (c.sub.x, c.sub.y, c.sub.z).

A method for augmenting transport services using event detection is provided. The method includes collection of first sensor data generated by various sensors associated with a plurality of vehicles. The first sensor data includes sensor outputs that indicate a plurality of rash driving events. The sensor outputs are augmented based on angular rotation to obtain augmented sensor outputs. A prediction model is trained based on the augmented sensor outputs. Target sensor data associated with a target vehicle is provided as input to the trained prediction model, and based on an output of the trained prediction model an occurrence of a rash driving event is detected in real-time or near real-time. Based on a count of rash driving events associated with the target driver within a cumulative driving distance, a driver score of the target driver is determined.

A system to reduce VRU unreferenced heading drift error is disclosed. The VRU comprises an IMU and a processor, which hosts first and second modules for bias cancelation. The first module reads inertial data from the IMU when the VRU is powered on; determines whether the VRU is static for a time period; if the VRU is static, corrects gyroscope bias by subtracting an initial bias value from a previous bias value; sets a predefined initial yaw value. The second module reads inertial data from the IMU during in-run operation of the VRU; updates roll, pitch and yaw data, based on input data from a sensor fusion algorithm; outputs updated roll, pitch and yaw data; determines whether the VRU is static for a time period; and if the VRU is static, corrects the bias by subtracting a current bias value, multiplied by a predefined parameter, from a previous bias value.

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.

A vehicle includes a transmission, a powerplant, an inertial measurement unit, and a controller. The transmission has an input shaft and an output shaft. The powerplant is configured to generate and deliver torque to the input shaft. The inertial measurement unit is configured to measure inertial forces exerted onto the vehicle. The controller is programmed to, in response to a demanded torque at the output shaft and a non-transient condition of the vehicle, control the torque at the output shaft based on a torque at the input shaft and a gear ratio of the step-ratio transmission. The controller is further programmed to, in response to the demanded torque at the output shaft and a transient condition of the vehicle, control the torque at the output shaft based on the inertial forces and a vehicle velocity.

A sensor unit includes a plurality of terminal members each of which includes a lead portion and an external terminal portion having an external connection end face, a sensor device connected to the lead portions, and a resin member that covers the sensor device and a part of the plurality of terminal members. The lead portion includes a thin wall portion having a thickness thinner than the external terminal portion and a protruding portion protruding from the thin wall portion to an external connection end face side. In a plan view from a direction where the terminal member and the sensor device overlap, the sensor device is disposed at a position overlapping the protruding portion and not overlapping the external terminal portion.

A method of providing road and vehicle diagnostics. The method includes providing a vehicle axle system having a first axle half shaft housing, a second axle half shaft housing and a differential housing. Attached one or more of said housings is one or more tri-axis accelerometers. In communication with the accelerometers is one or more data processors operably configured to receive and analyze data from the accelerometers. An occurrence of one or more road events is determined by one or more spikes in the Z-direction of said data collected from said accelerometers. A depth of the road event is determined by a magnitude of said positive and negative changes in acceleration of said spike in said Z-direction and a length of road event is determined by a span of said one or more spikes in said Z-direction. Once the road event is identified the time and geographic location of the road event is identified.