A proactive pedal algorithm is used to modify an accelerator pedal map to ensure the deceleration when the accelerator pedal is released matches driver expectation. Modifying the accelerator pedal map provides the driver of a vehicle the sensation that the vehicle resists moving when travelling in dense scenes with potentially high deceleration requirements and coasts easily in scenes with low deceleration requirements. The accelerator pedal map is modified based on a scene determination to classify other remote vehicles as in-lane, neighbor-lane, or on-coming.

The driving support apparatus includes a memory configured to store information representing a degree of familiarity with an environment for a driver of a vehicle; and a processor configured to detect an object existing around the vehicle based on a sensor signal representing a situation around the vehicle obtained by a sensor mounted on the vehicle, determine whether or not the object approaches the vehicle so that the object may collide with the vehicle, and notify the driver of the approach via a notification device mounted on the vehicle at a timing corresponding to the degree of familiarity with the environment for the driver of the vehicle, when it is determined that the object approaches the vehicle so that the object may collide with the vehicle.

A vehicle control device, a vehicle control method, and a vehicle control system according to the present invention obtain an inter-vehicle time based on a relative distance between a first vehicle traveling, in front of an own vehicle, in a second lane adjacent to a first lane in which the own vehicle travels and a second vehicle traveling in the second lane in front of the first vehicle and based on a relative velocity of the first vehicle relative to the second vehicle, obtain a relative acceleration of the first vehicle relative to the second vehicle, set the first vehicle as a high-stress vehicle based on a lane change space that is based on the inter-vehicle time, the relative acceleration, and a relative distance between the second vehicle and a third vehicle traveling in the first lane in front of the own vehicle, and output a control command for changing a driving state of the own vehicle based on a relative distance between the high-stress vehicle and the own vehicle. This makes it possible to improve the driving safety of a vehicle on a road with multiple lanes in each direction.

A vehicle control device controls travel of a vehicle by performing state transition for a plurality of control states having different driving assistance levels based on vehicle surrounding information. The vehicle control device comprises: an acquisition unit configured to acquire the vehicle surrounding information; and a control unit configured to control the state transition and braking operation of the vehicle based on the information acquired by the acquisition unit, wherein, based on the information, the control unit is configured to transition from a first control state set for travel of the vehicle to a second control state with a lower driving assistance level in a case where it is determined that another vehicle is crossing a lane line in front of the vehicle in a lane where the vehicle is traveling.

Vehicle alert and control systems and methods taking into account a detected road friction at a following vehicle and a predicted road friction by the following vehicle. The detected road friction between the following vehicle tires and the road surface may be assessed using a variety of methodologies and is used to compute a critical safety distance between the following vehicle and the preceding vehicle and a critical safety speed of the following vehicle. The predicted road friction ahead of the following vehicle may also be assessed using a variety of methodologies (lidar, camera, and cloud-based examples are provided) and is used to compute a warning safety distance between the following vehicle and the preceding vehicle and a warning safety speed of the following vehicle. These functionalities may be applied to vehicle/stationary object warning and response scenarios as well.

An impairment analysis (“IA”) computer system for alerting a first driver of a first vehicle to a driving hazard posed by a second vehicle operated by a second driver is provided. The IA computer system is associated with the first vehicle, and includes at least one processor in communication with at least one memory device. The at least one processor is programmed to: (i) receive second vehicle data including second driver data and second vehicle condition data, where the second vehicle data is collected by a plurality of sensors included on the first vehicle; (ii) analyze the second vehicle data by applying a baseline model to the second vehicle data; (iii) determine that the second vehicle poses a driving hazard to the first vehicle based upon the analysis; and/or (iv) generate an alert signal based upon the determination that the second vehicle poses a driving hazard to the first vehicle.

The present invention is configured to include an other-vehicle situation determination circuitry to estimate traveling situations of each of other vehicles traveling ahead of one's own vehicle and to determine that an alert situation is present when any of the other vehicles is estimated to be in a situation where a vehicle should slow down or change traffic lanes to a traffic lane in which one's own vehicle is traveling in order to avoid a collision with an object or a separate vehicle on a road; and a prediction function diagnosis circuitry, when receiving a determination result that the alert situation is present, diagnoses whether an other-vehicle behavior prediction function is sound or not based on whether or not an other-vehicle behavior prediction corresponding to the alert situation was able to be received from the other-vehicle behavior prediction function.

A deceleration assistance device including: a target information acquisition unit for acquiring information of a target located in front of a vehicle; a position estimation unit for estimating an estimated position of a deceleration object; a position recognition unit for recognizing a position of the deceleration object; and a control unit for executing, based on the estimated position, first deceleration control of decelerating a vehicle at a first deceleration and executing, based on the recognized position, second deceleration control of decelerating the vehicle at a second deceleration. The control unit executes processing of gradually changing a deceleration of the vehicle from the first deceleration toward the second deceleration when the deceleration control is caused to transition from the first to the second deceleration control, and when a difference exists between the first and second deceleration.

A road surface marking detection device is applied to a vehicle that includes a first camera that captures an image of at least an area forward of the vehicle, and a second camera that captures an image of at least an area forward of the vehicle and is able to capture a road surface region closer to the vehicle than a road surface region of which the image captured by the first camera. The road surface marking detection device includes a processor that, when a temporary stop sign forward of the vehicle is detected by the first camera, executes a process of detecting a road surface marking corresponding to the detected temporary stop sign by the second camera.

Systems and methods are provided for implementing personalized adaptive cruise control techniques in connection with, but not necessarily, autonomous and semi-autonomous vehicles. In accordance with one embodiment, a method comprises receiving first vehicle operating data and associated first environmental data of a plurality of vehicles; classifying the first vehicle operating data and the first environmental data into a plurality of driver type classifications; training a control policy model for each driver type classification based on the first vehicle operating data and the first environmental data; receiving a real-time classification of a target vehicle based on second vehicle operating data and associated second environmental data of the target vehicle; and output a trained control policy model the to target vehicle based on the real-time classification of the vehicle, wherein the target vehicle is controlled according to the trained control policy model.