A four-wheel drive force distribution apparatus for distributing drive forces to the wheels of a four-wheel drive vehicle, in which the distribution of drive force to the front inside wheel (2a) and the distribution of drive force to the rear inside wheel (3a) are adjusted based on a ground load of the front inside wheel (2a) and a ground load of the rear inside wheel (3a) when the vehicle is turning, and the distribution of drive force to the front inside wheel (2a) compared with distribution of drive force to the rear inside wheel (3a) is reduced the smaller the ratio of the ground load of the front inside wheel (2a) to the ground load of the rear inside wheel (3a) during turning.

A four-wheel drive force distribution apparatus for distributing drive forces to the wheels of a four-wheel drive vehicle, in which the distribution of drive force to the front inside wheel (2a) and the distribution of drive force to the rear inside wheel (3a) are adjusted based on a ground load of the front inside wheel (2a) and a ground load of the rear inside wheel (3a) when the vehicle is turning, and the distribution of drive force to the front inside wheel (2a) compared with distribution of drive force to the rear inside wheel (3a) is reduced the smaller the ratio of the ground load of the front inside wheel (2a) to the ground load of the rear inside wheel (3a) during turning.

Systems, methods and computer-readable media for spatio-temporal motion planning, including: receiving data defining a drivable area within a spatio-temporal space; generating a cost function to be minimized that includes one or more upper-bound cost terms, each upper-bound cost term approximating the upper-bound of a cost term for the trajectory for a spatial frame of the spatio-temporal space, each upper-bound cost term being expressed as one or more longitudinal-temporal cost terms and one or more lateral-temporal cost terms for the trajectory; and further cost terms for the trajectory for the spatio-temporal frames; computing, based on the constraints and cost function, a planned trajectory through the drivable area, the planned trajectory directory being optimized with respect to both spatial frame and the spatio-temporal frames. Optimizing the planned trajectory in the spatial frame in addition to the spatio-temporal frames can enable planning objectives for the spatial frame to be optimized, for example maintaining closeness to a reference path and minimizing lateral acceleration and directional oscillation.

Systems, methods and computer-readable media for spatio-temporal motion planning, including: receiving data defining a drivable area within a spatio-temporal space; generating a cost function to be minimized that includes one or more upper-bound cost terms, each upper-bound cost term approximating the upper-bound of a cost term for the trajectory for a spatial frame of the spatio-temporal space, each upper-bound cost term being expressed as one or more longitudinal-temporal cost terms and one or more lateral-temporal cost terms for the trajectory; and further cost terms for the trajectory for the spatio-temporal frames; computing, based on the constraints and cost function, a planned trajectory through the drivable area, the planned trajectory directory being optimized with respect to both spatial frame and the spatio-temporal frames. Optimizing the planned trajectory in the spatial frame in addition to the spatio-temporal frames can enable planning objectives for the spatial frame to be optimized, for example maintaining closeness to a reference path and minimizing lateral acceleration and directional oscillation.

Methods and system for vehicle control. The methods and systems determining actuator commands data based on a vehicle stability and motion control function. The vehicle stability and motion control function having planned trajectory data, current vehicle position data and current vehicle heading data as inputs, having the actuator commands data as an output and utilizing a model predicting vehicle motion including predicting vehicle heading data and predicting vehicle position data. The actuator commands data includes steering and propulsion commands. The actuator commands data includes differential braking commands for each brake of the vehicle to correct for any differential between the planned vehicle heading and the current vehicle heading data or the predicted vehicle heading data. The methods and systems output the actuator commands data to the actuator system.

Methods and system for vehicle control. The methods and systems determining actuator commands data based on a vehicle stability and motion control function. The vehicle stability and motion control function having planned trajectory data, current vehicle position data and current vehicle heading data as inputs, having the actuator commands data as an output and utilizing a model predicting vehicle motion including predicting vehicle heading data and predicting vehicle position data. The actuator commands data includes steering and propulsion commands. The actuator commands data includes differential braking commands for each brake of the vehicle to correct for any differential between the planned vehicle heading and the current vehicle heading data or the predicted vehicle heading data. The methods and systems output the actuator commands data to the actuator system.

An autonomous driving device is configured to switch a driving mode, and includes a destination setting type autonomous driving mode in which a vehicle is made to travel to a destination and a following road autonomous driving mode in which, when a destination is not set, the vehicle is made to travel along a road. The autonomous driving device includes a display unit and an electronic control unit. The electronic control unit is configured to, when the display unit is made to display a traveling route along a following road traveling route, make the display unit display a traveling route from a current position of the vehicle to a nearest branch road in front in a moving direction along the following road traveling route and a moving direction on the nearest branch road along the following road traveling route.

The present invention refers to a method for providing a multi-lane scenario driving support for an ego vehicle (10) in a traffic situation. Traffic surroundings are measured by an environment sensor system (14), whereby the traffic surroundings include data about traffic and free space within an ego lane (16) of the ego vehicle (10) and at least an adjacent lane (12a, 12b), and data about front proximity area (18) and rear proximity area (20) of the ego vehicle (10). A decision device (22) evaluates the measured traffic surroundings and decides a driving operation to be executed by the ego vehicle (10) based on at least one strategy. In the decision device (22) a cost function is used for choosing one of at least six strategies, the cost function being based on at least a core priority, whereby the core priority is to avoid collision of the ego vehicle (10) and not cause collision of the ego vehicle (10) with a third party vehicle (24. The decision device (22) by means of the cost function chooses one of at least the following six strategies: braking in the ego lane (16), to combine braking and steering within the ego lane (16) of the ego vehicle (10), steering within the ego lane (16) of the ego vehicle (10) to avoid an obstacle, to full-brake in the ego lane (16) of the ego vehicle (10), to combine braking and steering towards or when entering temporarily an adjacent lane (12a, 12b) and steering towards or when entering temporarily an adjacent lane (12a, 12b).

The present invention refers to a method for providing a multi-lane scenario driving support for an ego vehicle (10) in a traffic situation. Traffic surroundings are measured by an environment sensor system (14), whereby the traffic surroundings include data about traffic and free space within an ego lane (16) of the ego vehicle (10) and at least an adjacent lane (12a, 12b), and data about front proximity area (18) and rear proximity area (20) of the ego vehicle (10). A decision device (22) evaluates the measured traffic surroundings and decides a driving operation to be executed by the ego vehicle (10) based on at least one strategy. In the decision device (22) a cost function is used for choosing one of at least six strategies, the cost function being based on at least a core priority, whereby the core priority is to avoid collision of the ego vehicle (10) and not cause collision of the ego vehicle (10) with a third party vehicle (24. The decision device (22) by means of the cost function chooses one of at least the following six strategies: braking in the ego lane (16), to combine braking and steering within the ego lane (16) of the ego vehicle (10), steering within the ego lane (16) of the ego vehicle (10) to avoid an obstacle, to full-brake in the ego lane (16) of the ego vehicle (10), to combine braking and steering towards or when entering temporarily an adjacent lane (12a, 12b) and steering towards or when entering temporarily an adjacent lane (12a, 12b).

A turning control system for a vehicle having a driving motor to generate driving power may include: an error calculation unit that calculates an error between a lateral acceleration of the vehicle and a reference lateral acceleration; and a reduction torque calculation unit that calculates a torque reduction of the driving motor in order to reduce the error calculated by the error calculation unit.