Abstract: Crash detection in a road vehicle includes determining an impact location. Acceleration and yaw rate are measured, and occurrence of an impact is detected by comparing a total acceleration to an impact threshold. An impact angle is determined according to an arctangent of a ratio of lateral and longitudinal accelerations. A center-of-gravity to impact distance is determined according to vehicle mass, moment of inertia, acceleration, and yaw rate. When the yaw rate is less than a yaw threshold and the impact angle is within a predetermined range of an integer multiple of 90°, then the impact location is determined in response to a projection of the impact distance selected according to signs of the accelerations. Otherwise, the impact location is determined in response to a projection of the impact distance selected according to signs of the accelerations and a sign of the yaw rate.

Abstract: Light or moderate impacts against a vehicle are detected. Measured acceleration and yaw rate are compared to baseline thresholds in a baseline state. When the acceleration is above the threshold, a distance from an estimated impact location to a vehicle center-of-gravity is determined. When the determined distance is less than a vehicle edge distance, an impact-suspected state will be entered if at least one of the acceleration and yaw rate is above the respective threshold for a predetermined duration. In the impact-suspected state, a plurality of vehicle dynamic behaviors are monitored for confirming occurrence of the light impact. A return is made to the baseline state if the acceleration and yaw rate fail to remain above the respective threshold or if the calculated distance fails to remain less than the edge distance. The impact is detected when at least one of the behaviors confirms the occurrence.

Abstract: Crash detection in a road vehicle includes determining an impact location. Acceleration and yaw rate are measured, and occurrence of an impact is detected by comparing a total acceleration to an impact threshold. An impact angle is determined according to an arctangent of a ratio of lateral and longitudinal accelerations. A center-of-gravity to impact distance is determined according to vehicle mass, moment of inertia, acceleration, and yaw rate. When the yaw rate is less than a yaw threshold and the impact angle is within a predetermined range of an integer multiple of 90°, then the impact location is determined in response to a projection of the impact distance selected according to signs of the accelerations. Otherwise, the impact location is determined in response to a projection of the impact distance selected according to signs of the accelerations and a sign of the yaw rate.

Abstract: Light or moderate impacts against a vehicle are detected. Measured acceleration and yaw rate are compared to baseline thresholds in a baseline state. When the acceleration is above the threshold, a distance from an estimated impact location to a vehicle center-of-gravity is determined. When the determined distance is less than a vehicle edge distance, an impact-suspected state will be entered if at least one of the acceleration and yaw rate is above the respective threshold for a predetermined duration. In the impact-suspected state, a plurality of vehicle dynamic behaviors are monitored for confirming occurrence of the light impact. A return is made to the baseline state if the acceleration and yaw rate fail to remain above the respective threshold or if the calculated distance fails to remain less than the edge distance. The impact is detected when at least one of the behaviors confirms the occurrence.

Abstract: A vehicle control and computation system interfaces a task controller in the vehicle with a vehicle-specific computation manager in a cloud network. A wireless data channel couples the task controller and the cloud network. The task controller performs operational tasks in the vehicle using data-related resources in the cloud network. Upon initiating one of the operational tasks, the task controller sends a handshake signal to the computation manager as a resource request. The computation manager calls at least one cloud-based agent from a database of predetermined agents in response to the handshake signal. The task controller completes the operational task via communication with the called agent.

Abstract: A vehicle control and computation system interfaces a task controller in the vehicle with a vehicle-specific computation manager in a cloud network. A wireless data channel couples the task controller and the cloud network. The task controller performs operational tasks in the vehicle using data-related resources in the cloud network. Upon initiating one of the operational tasks, the task controller sends a handshake signal to the computation manager as a resource request. The computation manager calls at least one cloud-based agent from a database of predetermined agents in response to the handshake signal. The task controller completes the operational task via communication with the called agent.

Abstract: Energy consumption of a vehicle is optimized while traveling a route assembled of road segments between a first position and a destination. A speed profile generator is located at least partially off of the vehicle and uses an energy consumption model of the vehicle together with road grade data corresponding to the route to calculate an optimal speed profile. The speed profile specifies target speeds for respective locations on the route for traversing the route with an optimized energy consumption. The speed profile generator compares energy consumption for a plurality of feasible speed profile trajectories between a maximum trajectory and a minimum trajectory in order to identify the optimal speed profile. A speed updater is responsive to a current position of the vehicle and the optimal speed profile to initiate the target speed for the current position.

Abstract: A method for controlling an active suspension is disclosed. The method includes steps of determining a dimension of a road abnormality ahead of the vehicle; comparing the dimension with a vehicle dimension; responsive to the comparison, classifying the abnormality as one type of a plurality of predetermined types; responsive to a dimension of the abnormality, further classifying the abnormality as having a severity of one type of a plurality of predetermined types; and controlling the suspension responsive to the abnormality type and severity type.

Abstract: Road grade is modeled over a region in which a vehicle is driven on roadways having a grade that varies over a plurality of predetermined grade ranges. A succession of grade values are generated while operating the vehicle at a predetermined rate, wherein each grade value identifies a respective grade range then being encountered. A Markov chain road-grade model is updated in response to the succession of grade values, wherein the model represents respective elements of probability in a matrix of transition events from each predetermined grade range to a respective next-in-succession grade range. Each element of the matrix has a value ?i,j representing a weighted frequency of transition events from a first respective grade value to a second respective grade value divided by a weighted frequency of transition events initiating from the first respective grade value, so that the matrix successively approximates the road grade of the region.

Abstract: Vehicle apparatus adjusts a vehicle powertrain of the vehicle in response to a speed setpoint. An optimizer selects a control policy to periodically generate speed adjustments for applying to the speed setpoint to operate at increased efficiency. The control policy is based on a value function providing an optimized solution for a cost model and a transition probability model. The transition probability model corresponds to a driving state defined according to a plurality of dimensions including a time-of-day dimension and a geographic region dimension. The transition probability model and the control policy have inputs based on road grade and speed. The optimizer collects road grade data during routine driving of the vehicle to construct a observed transition probability model and uses divergence between the observed transition probability model and a set of predetermined transition probability models to identify a control policy for use during the routine driving.

Abstract: Vehicle apparatus includes a speed control for adjusting a vehicle powertrain of the vehicle in response to a speed setpoint. A grade estimator determines a road grade of a roadway where the vehicle is traveling. A traffic density estimator determines a density of traffic traveling on the roadway in the vicinity of the vehicle. An optimizer executes a selected control policy to periodically generate speed adjustments for applying to the speed setpoint to operate the vehicle powertrain at increased efficiency. The control policy is based on a value function providing an optimized solution for a cost model responsive to the determined road grade to generate an initial speed offset. The optimizer reduces the initial speed offset in proportion to the determined traffic density to generate the speed adjustments. The system minimizes negative impacts to overall traffic flow as well as any negative contribution to reduced fuel efficiency of surrounding traffic.

Abstract: A vehicle crash safety system includes a pre-crash sensing system configured for gathering and/or receiving target vehicle ride-height data, and at least one actuator operatively coupled to the sensing system and configured for adjusting a height of a portion of a host vehicle responsive to a command from the sensing system. A timing of the command is responsive to an estimated dynamic response time of the at least one actuator.

Abstract: A method for controlling an active suspension is disclosed. The method includes steps of determining a dimension of a road abnormality ahead of the vehicle; comparing the dimension with a vehicle dimension; responsive to the comparison, classifying the abnormality as one type of a plurality of predetermined types; responsive to a dimension of the abnormality, further classifying the abnormality as having a severity of one type of a plurality of predetermined types; and controlling the suspension responsive to the abnormality type and severity type.

Abstract: Energy consumption of a vehicle is optimized while traveling a route assembled of road segments between a first position and a destination. A speed profile generator is located at least partially off of the vehicle and uses an energy consumption model of the vehicle together with road grade data corresponding to the route to calculate an optimal speed profile. The speed profile specifies target speeds for respective locations on the route for traversing the route with an optimized energy consumption. The speed profile generator compares energy consumption for a plurality of feasible speed profile trajectories between a maximum trajectory and a minimum trajectory in order to identify the optimal speed profile. A speed updater is responsive to a current position of the vehicle and the optimal speed profile to initiate the target speed for the current position.

Abstract: Vehicle apparatus adjusts a vehicle powertrain of the vehicle in response to a speed setpoint. An optimizer selects a control policy to periodically generate speed adjustments for applying to the speed setpoint to operate at increased efficiency. The control policy is based on a value function providing an optimized solution for a cost model and a transition probability model. The transition probability model corresponds to a driving state defined according to a plurality of dimensions including a time-of-day dimension and a geographic region dimension. The transition probability model and the control policy have inputs based on road grade and speed. The optimizer collects road grade data during routine driving of the vehicle to construct a observed transition probability model and uses divergence between the observed transition probability model and a set of predetermined transition probability models to identify a control policy for use during the routine driving.

Abstract: Vehicle apparatus includes a speed control for adjusting a vehicle powertrain of the vehicle in response to a speed setpoint. A grade estimator determines a road grade of a roadway where the vehicle is traveling. A traffic density estimator determines a density of traffic traveling on the roadway in the vicinity of the vehicle. An optimizer executes a selected control policy to periodically generate speed adjustments for applying to the speed setpoint to operate the vehicle powertrain at increased efficiency. The control policy is based on a value function providing an optimized solution for a cost model responsive to the determined road grade to generate an initial speed offset. The optimizer reduces the initial speed offset in proportion to the determined traffic density to generate the speed adjustments. The system minimizes negative impacts to overall traffic flow as well as any negative contribution to reduced fuel efficiency of surrounding traffic.

Abstract: Road grade is modeled over a region in which a vehicle is driven on roadways having a grade that varies over a plurality of predetermined grade ranges. A succession of grade values are generated while operating the vehicle at a predetermined rate, wherein each grade value identifies a respective grade range then being encountered. A Markov chain road-grade model is updated in response to the succession of grade values, wherein the model represents respective elements of probability in a matrix of transition events from each predetermined grade range to a respective next-in-succession grade range. Each element of the matrix has a value ?i,j representing a weighted frequency of transition events from a first respective grade value to a second respective grade value divided by a weighted frequency of transition events initiating from the first respective grade value, so that the matrix successively approximates the road grade of the region.

Abstract: A method for controlling an active suspension includes steps of determining a dimension of a road abnormality ahead of the vehicle and comparing the dimension with a vehicle dimension. Responsive to the comparison, the abnormality is classified as one type of a plurality of predetermined types. Responsive to a height dimension of the abnormality, the abnormality is further classified as having one of a small, medium, or large severity. The suspension is controlled responsive to the type and severity.

Abstract: A vehicle crash safety system includes a pre-crash sensing system configured for gathering and/or receiving target vehicle ride-height data, and at least one actuator operatively coupled to the sensing system and configured for adjusting a height of a portion of a host vehicle responsive to a command from the sensing system. A timing of the command is responsive to an estimated dynamic response time of the at least one actuator.

Abstract: A method for controlling an active suspension includes steps of determining a dimension of a road abnormality ahead of the vehicle and comparing the dimension with a vehicle dimension. Responsive to the comparison, the abnormality is classified as one type of a plurality of predetermined types. Responsive to a height dimension of the abnormality, the abnormality is further classified as having one of a small, medium, or large severity. The suspension is controlled responsive to the type and severity.