A system and method automatically sets vehicle functions of a vehicle, while taking account of external influencing factors. The system includes at least one back-end server which is configured to receive vehicle data of the vehicle and, while taking account of at least part of the vehicle data, to determine data with regard to external influencing factors. The back-end server is configured to determine optimal settings of the vehicle functions of the vehicle, while taking account of the vehicle data and the data with regard to the external influencing factors, and to transmit the optimum settings of the vehicle functions to the vehicle. The vehicle is configured to control the vehicle functions such that the optimum vehicle functions determined by the back-end server are adopted.

A method of controlling inertial driving of a vehicle is provided. The method includes, when in an inertial driving guidance mode, obtaining information related to at least one event in consideration of a vehicle ahead situation, wherein the at least one event is a stop event, a deceleration event, or a re-acceleration event, determining a target shift stage corresponding to the at least one event based on the information related to the at least one event, and performing a shift control to the target shift stage.

A method of controlling coasting of an eco-friendly vehicle includes: determining at least one effective event among deceleration events configured with a target speed in a forward driving path; setting a closest effective event based on a current position among the at least one effective event as a first candidate event; determining whether at least one second candidate event corresponding to an event needed to be followed is present among remaining effective events except for the first candidate event of the at least one effective event; and, when the at least one second candidate event is present, determining a target event among the first candidate event and the second candidate event in consideration of a control start point.

A processing platform may obtain sensor data associated with a vehicle and manual input data associated with the vehicle. The processing platform may determine, based on the sensor data, automated control information. The processing platform may determine, based on the sensor data and the manual input data, a parameter associated with the vehicle. The processing platform may determine, based on the automated control information, a control rating associated with the parameter. The processing platform may determine whether the control rating satisfies a threshold for a period of time. The processing platform may cause, based on determining that the control rating satisfies the threshold for the period of time, at least one action to be performed.

Systems, methods, computer-readable media, techniques, and methodologies are disclosed for performing end-to-end, learning-based keypoint detection and association. A scene graph of a signalized intersection is constructed from an input image of the intersection. The scene graph includes detected keypoints and linkages identified between the keypoints. The scene graph can be used along with a vehicle's localization information to identify which keypoint that represents a traffic signal is associated with the vehicle's current travel lane. An appropriate vehicle action may then be determined based on a transition state of the traffic signal keypoint and trajectory information for the vehicle. A control signal indicative of this vehicle action may then be output to cause an autonomous vehicle, for example, to implement the appropriate vehicle action.

Enclosed are embodiments of motion control operations in various traffic scenarios in consideration of the kinematic factor for trajectory planning. In some embodiments, a method includes: determining a danger rating for at least one object identified in an environment, wherein the danger rating represents a perceived risk associated with a respective object; evaluating a set of hierarchical factors with respect to a traffic scenario, wherein a metric is derived for trajectories of the traffic scenario that quantifies passenger ride comfort based on the danger rating and the set of hierarchical factors; determining a motion control operation in the traffic scenario to increase the passenger ride comfort based on the metric; and augmenting a route planner of an autonomous vehicle with motion control operations in different traffic scenarios to increase the passenger ride comfort.

A fire fighting vehicle includes a front axle, a rear axle, an energy storage system, an engine, a first motor/generator, and a second motor/generator. In a first mode, (a) the engine is off and (b) at least one of the first motor/generator or the second motor/generator uses stored energy in the energy storage system to drive at least one of the front axle or the rear axle. In a second mode, (a) the engine provides a mechanical input the first motor/generator, (b) the first motor/generator uses the mechanical input to generate electricity, (c) the second motor/generator uses the electricity to drive at least one of the front axle or the rear axle. Any electricity generated by either the first motor/generator or second motor/generator in response to the mechanical input from the engine is never provided to the energy storage system to charge the energy storage system.

Techniques are provided for autonomous vehicle operation using linear temporal logic. The techniques include using one or more processors of a vehicle to store a linear temporal logic expression defining an operating constraint for operating the vehicle. The vehicle is located at a first spatiotemporal location. The one or more processors are used to receive a second spatiotemporal location for the vehicle. The one or more processors are used to identify a motion segment for operating the vehicle from the first spatiotemporal location to the second spatiotemporal location. The one or more processors are used to determine a value of the linear temporal logic expression based on the motion segment. The one or more processors are used to generate an operational metric for operating the vehicle in accordance with the motion segment based on the determined value of the linear temporal logic expression.

A driving support apparatus (12) has: a setting device (122) for setting a first target position (31) on the basis of a first sign object (21), if the first sign object requesting a vehicle (1) to stop is detected; and a supporting device (123) for performing a first deceleration control for decelerating the vehicle to a first target speed before the vehicle reaches the first target position, if a second sign object (22) representing a stop position is detected during a period when the first decelerating control is performed, the setting device sets a second target position (32) on the basis of the second sign object and the supporting device performs a second decelerating control for decelerating the vehicle to a second target speed before the vehicle reaches the second target position.

Location polygons are defined along traffic lanes and parking spaces to facilitate determination of the location of a vehicle relative to features associated with the location polygons. The location polygons are used, in one application, to identity entrance and exit of a special toll lane along a roadway, and to ensure that the vehicle properly enters and exits the tolling lane. The location polygons define geofenced regions, and each definition for a geofenced region can include one or more rules that are used to evaluate location information reported by a user’s equipment. The rules dictate whether an action it taken or inhibited, such as charging a toll or not charging a toll, based on other location information reported by the user’s equipment.