A method for operating an electrically operated or also electrically operable motor vehicle provided with a rechargeable electric energy storage device associated with the drive motor of the motor vehicle. A target charging state is determined for the energy storage device and an operating strategy is determined for a route that is calculated, entered or predicted for the next trip, by which recuperative deceleration is enabled with a specifiable minimum amount for deceleration processes occurring along the route. A total mass of the motor vehicle, including optionally a trailer connected to the motor vehicle, deviating from an input normal value and an air resistance of the motor vehicle deviating from a predetermined normal value are taken into account.

While operating a host vehicle in a lane, a target vehicle is detected entering the lane in front of the vehicle. A trajectory of the target vehicle is predicted based on sensor data. Upon determining that the target vehicle will pass through the lane based on the predicted trajectory, the host vehicle is operated based on determining a presence or an absence of a lead vehicle. Upon determining that the target vehicle will remain in the lane based on the predicted trajectory, the host vehicle is operated with the target vehicle as the lead vehicle.

According to one aspect, systems and techniques for lane selection may include receiving a current state of an ego vehicle and a traffic participant vehicle, and a goal position, projecting the ego vehicle and the traffic participant vehicle onto a graph network, where nodes of the graph network may be indicative of discretized space within an operating environment, determining a current node for the ego vehicle within the graph network, and determining a subsequent node for the ego vehicle based on identifying adjacent nodes which may be adjacent to the current node, calculating travel times associated with each of the adjacent nodes, calculating step costs associated with each of the adjacent nodes, calculating heuristic costs associated with each of the adjacent nodes, and predicting a position of the traffic participant vehicle.

In a drive assist system, a map data acquiring section acquires at least one of a driver's operation ability and a load of a vehicle. An adjustment section determines an assist control amount as a control parameter of drive assist for the vehicle so that a degree of the driver's operation is increased according to reduction of the driver's operation ability or increasing of the load of the vehicle. An assist control amount calculation section transmits the assist control amount to a steering motor and a notification section so as to execute the drive assist for the vehicle.

Aspects of the disclosure relate to testing predictions of an autonomous vehicle relating to another vehicle or object in a roadway. For instance, one or more processors may plan to maneuver the autonomous vehicle to complete an action and predict that the other vehicle will take a responsive action. The autonomous vehicle is then maneuvered towards completing the action in a way that would allow the autonomous vehicle to cancel completing the action without causing a collision between the first vehicle and the second vehicle, and in order to indicate to the second vehicle or a driver of the second vehicle that the first vehicle is attempting to complete the action. Thereafter, when the first vehicle is determined to be able to take the action, the action is completed by controlling the first vehicle autonomously using the determination of whether the second vehicle begins to take the particular responsive action.

Provided is an obstacle avoidance apparatus that can specify a distance between a subject vehicle and an obstacle when making the subject vehicle avoid the obstacle. An obstacle avoidance apparatus includes: an obstacle movement predictor that predicts movement of the obstacle; and a constraint generator that establishes a constraint on a state quantity or a control input of the subject vehicle by determining whether to steer right or left around the obstacle and defining a no-entry zone for preventing the subject vehicle from colliding with the obstacle. The constraint generator incorporates, into the no-entry zone, an area to the left of the obstacle when determining to steer right around the obstacle, and incorporates, into the no-entry zone, an area to the right of the obstacle when determining to steer left around the obstacle.

A vehicle traveling control system according to the example in the present disclosure communicates with an automatic operation control system which drafts a traveling plan of the vehicle, and performs an automatic traveling control for automatically running the vehicle along the traveling plan received from the automatic operation control system. The vehicle traveling control system predicts a risk based on information about surrounding environment of the vehicle, and performs, when the risk is predicted, a risk avoidance control to intervene in the automatic traveling control in order to avoid the risk. When the risk avoidance control is executed, the vehicle traveling control system transmits information on the risk avoidance control to the automatic operation control system.

Embodiments herein can determine an optimal route for an autonomous electric vehicle. The system may score viable routes between the start and end locations of a trip using a numeric or other scale that denotes how viable the route is for autonomy. The score is adjusted using a variety of factors where a learning process leverages both offline and online data. The scored routes are not based simply on the shortest distance between the start and end points but determine the best route based on the driving context for the vehicle and the user.

In some examples, a controller receives information of a route of a vehicle, and selects a first parameter set from among a plurality of parameter sets based on the route of the vehicle, the plurality of parameter sets corresponding to different conditions of usage of the vehicle, where each parameter set of the plurality of parameter sets includes one or more parameters that control adjustment of one or more respective adjustable elements of the vehicle. The controller causes application of the first parameter set to control a setting of the one or more adjustable elements of the vehicle.

A driver assistance method for an ego vehicle (EGO) travelling in a traffic lane, includes: identifying traffic surrounding the ego vehicle in the same traffic lane as the ego vehicle and in adjacent parallel lanes travelling in the same direction; determining a virtual barycentric target, including calculating a position of the virtual barycentric target, a speed of the virtual barycentric target, and an acceleration of the virtual barycentric target; calculating a longitudinal speed setpoint of the ego vehicle, an acceleration setpoint, and a torque setpoint, the longitudinal speed setpoint being a function of the position of the virtual barycentric target, the speed of the virtual barycentric target, and the acceleration of the virtual barycentric target.