When a scheduled traveling route is changed during travelling of an electrical vehicle, a management ECU of the electrical vehicle maintains control of a travelling mode based on a travelling plan created based on the scheduled traveling route before the change, in accordance with a result of comparison between first attribute information and second attribute information, the first attribute information indicating an attribute of at least one section, which is located within a predetermined distance from a section in which the electrical vehicle is located and located in a vehicle travelling direction, of the scheduled traveling route before the change, the second attribute information indicating an attribute of at least one section, which is located within a predetermined distance from a section in which the electrical vehicle is located and located in a vehicle travelling direction, of the scheduled traveling route after the change.

A management ECU of an electrical vehicle creates a travelling plan in which any one of travelling modes is assigned to each of traveling sections of a scheduled traveling route from a current position of the electrical vehicle to a destination and control the travelling modes based on the travelling plan. When a total estimated value of an amount of electric power required for traveling in each of the traveling sections in a first traveling mode exceeds a current SOC, the management ECU extracts a section in which an output estimated value exceeds a second predetermined value as a planning target section, and creates the traveling plan in which a second traveling mode in which a usage amount of electric power of a storage battery is smaller than that in the first traveling mode is assigned preferentially to a section farther from the electrical vehicle among the planning target sections.

A system for providing a speed profile of a self-driving vehicle includes a vehicle driving information prediction device, and a speed profile generation device, wherein the vehicle driving prediction device includes a navigation unit configured to set information on a drive route and a target travel time, a 3D map information provision unit configured to search for gradient information of the drive route set by the navigation unit, and a vehicle driving information provision unit, and wherein the speed profile generation device includes a vehicle energy consumption calculation unit configured to calculate energy consumption at a current speed of the vehicle when the vehicle runs along the set drive route, and a speed profile calculation unit configured to calculate a distance-based target speed profile by executing a dynamic programming algorithm.

A system and method for predicting a driver's situational awareness that includes receiving driving scene data associated with a driving scene of an ego vehicle and eye gaze data to track a driver's eye gaze behavior with respect to the driving scene. The system and method also include analyzing the eye gaze data and determining an eye gaze fixation value associated with each object that is located within the driving scene and analyzing the driving scene data and determining a situational awareness probability value associated with each object that is located within the driving scene that is based on a salience, effort, expectancy, and a cost value associated with each of the objects within the driving scene. The system and method further include communicating control signals to electronically control at least one component based on the situational awareness probability value and the eye gaze fixation value.

Apparatuses, systems, and methods are provided for the utilization of vehicle control systems to cause a vehicle to take preventative action responsive to the detection of a near short term adverse driving scenario. A vehicle control system may receive information corresponding to vehicle operation data and ancillary data. Based on the received vehicle operation data and the received ancillary data, a multi-dimension risk score module may calculate risk scores associated with the received vehicle operation data and the received ancillary data. Subsequently, the vehicle control systems may cause the vehicle to perform at least one of a close call detection action and a close call detection alert to lessen the risk associated with the received vehicle operation data and the received ancillary data.

A driving assistance system includes a processor, a storage device, and an output device. The storage device retains traveling environment information indicating a traveling environment of a vehicle and driving characteristic information indicating driving characteristics of a driver of the vehicle. The processor generates a message corresponding to the traveling environment of the vehicle based on the traveling environment information, and generates a message corresponding to the driving characteristics of the driver of the vehicle based on the driving characteristic information. The output device outputs the message corresponding to the traveling environment and the message corresponding to the driving characteristics in different aspects.

An intelligent driving method comprising: obtaining feature parameters of a vehicle at a current moment and a road attribute of a driving scenario of the vehicle in a preset future time period; comparing the feature parameters at the current moment with feature parameters of a standard scenario in a scenario feature library; comparing the road attribute of the driving scenario of the vehicle in the preset future time period with a road attribute of the standard scenario in the scenario feature library; determining a total similarity of each scenario class to a driving scenario of the vehicle at the current moment based on comparing results; determining, as the driving scenario at the current moment, a first scenario class with a highest total similarity in N scenario classes; and controlling, based on the determining result, the vehicle to perform intelligent driving.

Systems and methods for forecasting trajectories of objects. The method includes obtaining a prediction model trained to predict future trajectories of objects. The prediction model is trained over a first prediction horizon selected to encode inertial constraints in a predicted trajectory and over a second prediction horizon selected to encode behavioral constraints in the predicted trajectory. The method also include generating a planned trajectory of an autonomous vehicle by receiving state data corresponding to the autonomous vehicle, receiving perception data corresponding to an object, predicting a future trajectory of the object based on the perception data and the prediction model, and generating the planned trajectory of the autonomous vehicle based on the future trajectory of the object and the state data.

A control apparatus for controlling a vehicle includes a driving motor configured to drive the vehicle by outputting motor torque based on a supply voltage from a battery, and an engine configured to drive the vehicle by outputting engine torque. The control apparatus may acquire driving mode data which is calculated based on traffic information from the current position to the destination of the vehicle and dimension information of the vehicle, and control the vehicle to drive to the destination according to a driving mode which is determined by applying a travelling condition of the vehicle to the acquired driving mode data, where the power distribution ratio of the motor torque to the engine torque is reflected in the driving mode data.

The present invention obtains a controller capable of handling special travel made by a straddle-type vehicle. The present invention also obtains a rider-assistance system including such a controller. The present invention further obtains a control method capable of handling special travel made by a straddle-type vehicle.

Surrounding environment information of a straddle-type vehicle (100) is acquired on the basis of output of at least one surrounding environment detector (11). Then, based on the surrounding environment information, presence or absence of slip-by travel between rows of vehicles made by the straddle-type vehicle (100) is analyzed. As a result, rider-assistance operation that corresponds to the analysis result is executed.