Method and device for optimized autonomous driving, wherein a trajectory for the actuation of the vehicle is determined. A profile of the trajectory is defined by a bending line of a bending band. The bending line is preferably determined by the finite element method, in particular according to the principle of virtual shifting, and achieves an optimization goal and satisfies a boundary condition. The boundary condition is defined in accordance with a profile of a roadway for the vehicle. The optimization goal is defined by a desired driving property of the vehicle.

Method and device for optimized autonomous driving, wherein a trajectory for the actuation of the vehicle is determined. A profile of the trajectory is defined by a bending line of a bending band. The bending line is preferably determined by the finite element method, in particular according to the principle of virtual shifting, and achieves an optimization goal and satisfies a boundary condition. The boundary condition is defined in accordance with a profile of a roadway for the vehicle. The optimization goal is defined by a desired driving property of the vehicle.

The present invention discloses an intelligent vehicle platoon lane change performance evaluation method. First, an intelligent vehicle platoon lane change performance test scenario is established; secondly, a three-degree of freedom nonlinear dynamics model is established according to motion characteristics of intelligent vehicles in a platoon lane change process; further, an improved adaptive unscented Kalman filter algorithm is utilized to perform filter estimation on state variables of positions and velocities of platoon vehicles; and finally, based on accurately recursive vehicle motion state parameters, evaluation indexes for platoon lane change performance are proposed and quantified, and an evaluation system for platoon lane change performance is constructed. According to the method proposed in the present invention, the problem of lacking platoon lane change performance quantitative evaluation at present is solved, vehicle motion state parameters can be measured in a high-precision and comprehensive manner, multi-dimensional platoon lane change performance evaluation indexes are quantified and output, and comprehensive, accurate, and reliable scientific quantitative evaluation for platoon lane change performance is achieved.

The present invention discloses an intelligent vehicle platoon lane change performance evaluation method. First, an intelligent vehicle platoon lane change performance test scenario is established; secondly, a three-degree of freedom nonlinear dynamics model is established according to motion characteristics of intelligent vehicles in a platoon lane change process; further, an improved adaptive unscented Kalman filter algorithm is utilized to perform filter estimation on state variables of positions and velocities of platoon vehicles; and finally, based on accurately recursive vehicle motion state parameters, evaluation indexes for platoon lane change performance are proposed and quantified, and an evaluation system for platoon lane change performance is constructed. According to the method proposed in the present invention, the problem of lacking platoon lane change performance quantitative evaluation at present is solved, vehicle motion state parameters can be measured in a high-precision and comprehensive manner, multi-dimensional platoon lane change performance evaluation indexes are quantified and output, and comprehensive, accurate, and reliable scientific quantitative evaluation for platoon lane change performance is achieved.

A vehicle height adjustment control apparatus is provided for compensating for vehicle pulls including a recognition device that recognizes that a vehicle is driven straight, a determination device that determines whether the vehicle pulls of the vehicle occur, in response to recognizing that the vehicle is driven straight, and a controller that generates a warning message and calculates compensation height control information of the vehicle in response to determining that the vehicle pulls occur.

Techniques for vehicle deceleration planning are discussed. The techniques include determining a first location and a first velocity of a vehicle. The techniques further include determining a second location and a second velocity of an object. Based on the first location, the second location, the first velocity, and the second velocity, a relative stopping distance between the vehicle and the object can be determined. If the relative stopping distance is less than a threshold distance, the first maximum deceleration value can be increased to a second maximum deceleration value, and the techniques determine a trajectory for the vehicle based at least in part on the second maximum deceleration value.

Techniques for vehicle deceleration planning are discussed. The techniques include determining a first location and a first velocity of a vehicle. The techniques further include determining a second location and a second velocity of an object. Based on the first location, the second location, the first velocity, and the second velocity, a relative stopping distance between the vehicle and the object can be determined. If the relative stopping distance is less than a threshold distance, the first maximum deceleration value can be increased to a second maximum deceleration value, and the techniques determine a trajectory for the vehicle based at least in part on the second maximum deceleration value.

An autonomous vehicle control system is provided. The control system may include a plurality of cameras to acquire a plurality of images of an area in a vicinity of a vehicle; and at least one processing device configured to: recognize a curve to be navigated based on map data and vehicle position information; determine an initial target velocity for the vehicle based on at least one characteristic of the curve as reflected in the map data; adjust a velocity of the vehicle to the initial target velocity; determine, based on the plurality of images, observed characteristics of the curve; determine an updated target velocity based on the observed characteristics of the curve; and adjust the velocity of the vehicle to the updated target velocity.

An autonomous vehicle control system is provided. The control system may include a plurality of cameras to acquire a plurality of images of an area in a vicinity of a vehicle; and at least one processing device configured to: recognize a curve to be navigated based on map data and vehicle position information; determine an initial target velocity for the vehicle based on at least one characteristic of the curve as reflected in the map data; adjust a velocity of the vehicle to the initial target velocity; determine, based on the plurality of images, observed characteristics of the curve; determine an updated target velocity based on the observed characteristics of the curve; and adjust the velocity of the vehicle to the updated target velocity.

A method of controlling operation of a vehicle includes monitoring at least one of a location and a route of the vehicle when the vehicle is in a first operating state, receiving map data related to an area around at least one of the location and the route, and identifying, based on the map data, one or more map attributes indicative of one or more features of the area. The method also includes comparing the one or more map attributes to at least one reference attribute, and based on the one or more map attributes matching the at least one reference attribute, causing the vehicle to enter a second operating state when the vehicle is in the area.