Systems and methods for coordinating and controlling vehicles, for example heavy trucks, to follow closely behind each other, or linking to form a platoon. In one aspect, on-board controllers in each vehicle interact with vehicular sensors to monitor and control, for example, relative distance, relative acceleration or deceleration, and speed. In some aspects, vehicle onboard systems supply various data (breadcrumbs) to a Network Operations Center (NOC), which in turn provides data (authorization data) to the vehicles to facilitate platooning. The NOC suggests vehicles for platooning based on, for example, travel forecasts and analysis of relevant roadways to identify platoonable roadway segments. The NOC also can provide traffic, roadway, weather, or system updates, as well as various instructions. In some aspects, a mesh network ensures improved communication among vehicles and with the NOC. In some aspects, a vehicle onboard system may provide the authorization data.

An electronic control device comprises a sensor information acquisition unit which acquires state quantities including a position of an object to be measured using an output of a plurality of sensors, or calculates the state quantities of the object to be measured by using the output of the plurality of sensors, a storage unit which stores position determination information as a condition of a classification related to the position, a position determination unit which classifies the object to be measured by using the position determination information, and a fusion unit which determines, by referring to the state quantities, a match among a plurality of the objects to be measured which are classified identically by the position determination unit and which were measured by different sensors, and fuses the positions of the plurality of objects to be measured determined to be a match, wherein the objects to be measured are objects that can be measured by each of the plurality of sensors.

A vehicle periphery monitoring device stores images photographed with a front camera, a rear camera, a left side camera, and a right side camera as a past front image, a past rear image, a past left side image, and a past right side image, respectively. When a vehicle travels while making a turn, the vehicle periphery monitoring device generates an underfloor image indicating a condition of an underfloor of the vehicle using at least one of the past front image and the past rear image and at least one of the past left side image and the past right side image. The vehicle periphery monitoring device displays the generated underfloor image on a display.

A vehicle driving intention prediction method includes that a road intention and a lane intention of a target vehicle are determined based on a driving feature of a surrounding vehicle relative to the target vehicle, a driving feature of the target vehicle relative to a road, and a driving feature of the target vehicle relative to a lane, and then a driving intention of the target vehicle is determined based on the road intention and the lane intention of the target vehicle. The driving intention of the target vehicle is determined by predicting a multi-level intention, for example, the lane intention or the road intention, of the target vehicle.

A receding horizon state estimator estimates state of a vehicle such as to reduce total communication cost of acquiring external measurements over a prediction horizon, in which state estimation accuracy for a time step is a function of state estimation accuracy for a previous time step. For each time step of the prediction horizon, estimator selects a subset of external sensors with external measurements sufficient to estimate the state with accuracy satisfying the constraint on state estimation accuracy for the corresponding time step while reducing a total communication cost of acquiring the external measurements over the prediction horizon. The estimator requests the external measurements from the subset of external sensors determined for a current time step and estimates the state of the vehicle using the internal and the requested external measurements.

A method for operating an advanced driver assistance systems (ADAS) includes determining a distance between an object and a vehicle, selecting a path estimation methodology from multiple path estimation methodologies based at least in part on a distance between the vehicle and the object, and activating at least one ADAS action in response to determining that the object intersects the estimated path.

An information processing device that provides information to an occupant who performs a given activity in a vehicle has a controller. The controller predicts an acceleration applied to the vehicle within a predetermined period, gives notice to the occupant when a value related to the predicted acceleration exceeds a threshold value, and determines the threshold value based on a type of the activity performed in the vehicle.

The invention provides a device for controlling a vehicle module with plausible information, which contains a multicore safety processor, configured to check processed information for plausibility. A control unit for a vehicle module is also provided, which contains a power processor, the evaluated information of which is checked for plausibility via an information interface in the safety processor of the device according to the invention. A driver assistance system process is also provided, in which a control unit according to the invention is used.

Among other things, we describe techniques for redundancy in autonomous vehicles. For example, an autonomous vehicle can include two or more redundant autonomous vehicle operations subsystems.

A method for fusing state data via a control unit. State data of a first mobile unit and of an object ascertained via a sensor system of the first mobile unit are received. State data of an object ascertained via a sensor system of a second mobile unit and/or state data of the second mobile unit, transmitted via a communication link from the second mobile unit to the first mobile unit, are received. A node is created in a time-position diagram for each set of received state data of the first mobile unit, the second mobile unit, and the objects. A data optimization of the state data ascertained by the first mobile unit and/or by the second mobile unit is carried out. An optimization problem is created based on the optimized state data ascertained by the first mobile unit and the optimized state data received from the second mobile unit.