An automated method of controlling a speed of a vehicle includes identifying parameters of a driving instance of the vehicle; identifying a predetermined profile that is applicable to the driving instance based on the identified parameters; identifying a predetermined speed policy applicable to the driving instance based on the identified profile; and implementing the identified speed policy during the driving instance. The method may be repeated during the driving instance, whereby the speed policy that is implemented is automatically updated when one or more changes in the identified parameters cause a different predetermined speed policy to be identified. Parameter may include driver parameters (e.g., driver age and driver experience); vehicle parameters (e.g., vehicle age, mileage, and tire wear) tire maintenance information); behavior parameters (e.g., speed, acceleration, hard braking of the vehicle, following distance, swerving, and cornering); and circumstance parameters (e.g., time of day, road information, inclement weather, and traffic congestion).

This disclosure relates to a system and method for determining vehicle operator preparedness for vehicles that support both autonomous operation and manual operation. The system includes sensors configured to generate output signals conveying information related to vehicles and their operation. During autonomous vehicle operation, the system gauges the level of responsiveness of an individual vehicle operator through challenges and corresponding responses. Based on the level of responsiveness, a preparedness metric is determined for each vehicle operator individually.

A driving assistance apparatus includes a clutch provided between a drive source and a transmission, a clutch operator with which a driver who drives a vehicle disengages the clutch, a clutch operation detector that detects that the clutch is disengaged, a shift operator with which the driver sets the transmission at least to a neutral position, a shift position detector that detects that the transmission is in the neutral position, a low-speed motor, and a controller that controls a drive force of the low-speed motor. The controller includes a driving mode setter that sets a driving mode of the vehicle to a motor driving mode when the clutch is detected to be disengaged or the transmission is detected to be in the neutral position. The controller stops the drive source and starts the low-speed motor when the driving mode is set to the motor driving mode.

A vehicle control method for executing a sailing stop control when a drive source stop condition is established while a vehicle is traveling. The sailing stop control stops a drive source of the vehicle and releases an engaging element provided between the drive source and a drive wheel such that the vehicle travels under inertia. The vehicle control method acquires information on a road on which the vehicle will travel, and then determines whether there is a section on a route where the sailing stop control can be executed based on the information. Upon determining a section exist capable of the sailing stop control, the vehicle control method estimates a power shortage amount, which is a shortage amount of power during sailing stop control, based on the information, and charges a battery with power required to cover the power shortage amount prior to starting the sailing stop control.

The travel control device, when determining that the traffic congestion is occurred in the other lane, detects, behind another vehicle in the other lane, an approach space; determines whether the approach space meets a predetermined condition; when determining that the approach space meets the predetermined condition, sets the target posture of the own vehicle at the target position behind the other vehicle in the other lane based on the shape of the approach space; and generates a target traveling trajectory from a current position of the own vehicle to the target position. The travel control device controls the motion of the own vehicle so that the own vehicle tracks the target traveling trajectory.

A traffic monitoring system includes a storage device that stores a program and a hardware processor, in which the hardware processor executes a program stored in the storage device, thereby acquiring a position of a mobile object based on information from a detection device for detecting the position of the mobile object, generating information on a density distribution in which index values having a distribution according to the position of the mobile object are superimposed on one another for a plurality of mobile objects, and predicting whether a target mobile object is likely to enter a gap between two of the mobile objects based on a temporal change in density value indicated by the information on a density distribution.

An apparatus of determining an optimal velocity of a vehicle, may include an information receiving unit configured to receive and provide vehicle traveling information and traveling environment information which are state variables representing vehicle states required to determine a target velocity for optimizing vehicle fuel economy; and an optimal velocity determination unit configured to determine the target velocity in accordance with a vehicle traveling environment by use of a state variable and reward estimation model and a Q table having values according to the state variables and a control input, from the vehicle traveling information and the traveling environment information provided by the information receiving unit, and a method of determining an optimal velocity of a vehicle.

The present disclosure is directed to apparatus, methods, and non-transitory storage medium for controlling the maximum speed of a vehicle as that vehicle travels along a route. Apparatus and methods consistent with the present disclosure may receive location information from an electronic device that is located at a vehicle and may provide information to the electronic device at that controls the maximum speed of the vehicle as speed limits change along the route.

System and methods are provided that contextually define interest index requirements for shared and autonomous vehicles. An interest index is computed based on detected contextual behavioral patterns of pedestrians such as the trajectory a candidate passenger is walking given a locational context. The use of the interest index allows users of shared vehicles to benefit from an enhanced user experience that seamlessly unlocks and/or provides access to features for autonomous vehicles based on the interest index.

Systems, apparatuses and methods may provide for technology that conducts a real-time analysis of interior sensor data associated with a vehicle, exterior sensor data associated with the vehicle and environmental data associated with the vehicle. Additionally, the technology may determine whether a hazard condition exists based on the real-time analysis, wherein the hazard condition includes a deviation of a current behavior waveform from a reference behavior waveform by a predetermined amount. In one example, a safety measure is triggered with respect to the vehicle if the hazard condition exists. The safety measure may be selected based on a reaction time constraint associated with the hazard condition.