A system for determining a content of a message used to coordinate interactions among vehicles can include a processing device and a memory. The memory can store a discretization module and a communications module. The discretization module can include instructions that when executed by the processing device cause the processing device to: (1) analyze a critical maneuver trajectory of an ego vehicle and a current trajectory of another vehicle to determine an existence of a critical maneuver situation and (2) produce a discretized representation of a portion of the critical maneuver trajectory of the ego vehicle. A form of the discretized representation can be based on the existence of the critical maneuver situation. The communications module can include instructions that when executed by the processing device cause the processing device to communicate the discretized representation as the content of the message used to coordinate interactions among vehicles.

A device has a vicinity monitoring unit that generates vicinity conditions information representing conditions of the vicinity of a moving apparatus; a driver monitoring unit that generates line of sight information representing a line of sight region of a line of sight direction of a driver of the moving apparatus; a detection unit that detects the number and positions of subjects that are present in a first region set in a predetermined direction of the moving apparatus by using the vicinity conditions information; and a control unit that executes a first notification in relation to the subject when the subject is included in the line of sight region, and to execute a suppressed second notification in relation to the subject when the subject is not included in the line of sight region.

A suspension system and associated control methods for improving the effectiveness of driver assistance systems is disclosed where the driver assistance systems can generate and send requests to a suspension control unit (SCU) of the suspension system to actuate (e.g., close) one or more comfort valves in the suspension system to increase the roll stiffness and/or pitch stiffness of the suspension system when the driver assistance systems are taking corrective action. As part of a two-way communication between the suspension control unit (SCU) and the driver assistance systems, the suspension control unit (SCU) communicates target stiffnesses and/or calculated effective stiffnesses to the driver assistance systems, which is used to update the vehicle stability models used by the driver assistance systems.

An example calibration system may include a tractor and a calibration unit. The tractor may include a first sensor and a second sensor. The calibration unit may include a processing unit and a non-transitory computer-readable medium containing instructions to direct the processing unit to: (1) determine a first estimate for a tractor parameter based upon signals received from the first sensor; (2) determine a second estimate for the tractor parameter based upon signals received from the second sensor; (3) determine a third estimate for the tractor parameter based upon a combination of the first estimate and the second estimate; (4) determine a tractor parameter correction based upon the second estimate and the third estimate; and (4) apply the tractor parameter correction to the second sensor to control positioning of the tractor.

Techniques for controlling a vehicle based on a collision avoidance algorithm are discussed herein. The vehicle receives sensor data and can determine that the sensor data represents an object in an environment through which the vehicle is travelling. A computing device associated with the vehicle determines a collision probability between the vehicle and the object at predicted locations of the vehicle and object at a first time. Updated locations of the vehicle and object can be determined, and a second collision probability can be determined. The vehicle is controlled based at least in part on the collision probabilities.

A dynamic velocity planning method for an autonomous vehicle is performed to plan a best velocity curve of the autonomous vehicle. An information storing step is performed to store an obstacle information, a road information and a vehicle information. An acceleration limit calculating step is performed to calculate the vehicle information according to a calculating procedure to generate an acceleration limit value range. An acceleration combination generating step is performed to generate a plurality of acceleration combinations according to the obstacle information, the road information, and the acceleration limit value range. An acceleration filtering step is performed to filter the acceleration combinations according to a jerk threshold and a jerk switching frequency threshold to obtain a selected acceleration combination. An acceleration smoothing step is performed to execute a driving behavior procedure to adjust the selected acceleration combination to generate the best velocity curve.

An occupancy grid manager having a non-uniform occupancy which includes a plurality of cells, configurable in a plurality of cell sizes, each cell representing a region of an environment of a vehicle; and one or more processors, configured to determine one or more context factors; select a cell size for a cell of the plurality of cells based on the one or more context factors; process sensor data provided by one or more sensors; and determine a probability that the cell of the plurality of cells is occupied based on the sensor data.

A system is provided for vehicle range prediction. The system determines a change in mass to a vehicle while driving. Additionally, the system calculates a vehicle load in response to determining the change in mass and adjusts a vehicle range in response to calculating the vehicle load. The vehicle range is indicative of a distance in which the vehicle is predicted to travel with a remaining fuel. The adjusted vehicle range is based on the vehicle load.

A method of controlling a vehicle having wheels provided with tires resting on a surface, the method using a model of the physical behavior of each tire as a function of a sideslip angle (β.sub.ij) for each tire relative to the surface. The model is obtained by implementing an adaptive algorithm that selectively applies an affABREGEine model (Z1), a DUGOFF model (Z2), or a constant model (Z3).

The method provides for one or more processors to receive traffic information and passing vehicle information associated with a portion of a roadway in which a passing vehicle approaches and travels through the portion of the roadway. The one or more processors predict travel positions of passing vehicles, based on the traffic information and passing vehicle information. The one or more processors determine an impassible space within an existing lane of the roadway and create virtual lane definitions based on the predicting and the traffic information, in which the lane definitions include an optimum number of lanes, a width of respective lanes, and a lane type, and the one or more processors transmit the lane definitions to the passing vehicles based on a correspondence between a type and width of a respective vehicle and the type and width of respective lane definitions.