An integrated control system of a vehicle includes: a power manager that receives power of a vehicle and supplies power to a first sensor, a second sensor, and a third sensor connected to an accelerator pedal, a brake pedal, and a transmission, respectively; a sensor signal receiver that receives an accelerator pedal output signal, a brake pedal output signal, and a transmission output signal from the first sensor, second sensor, and third sensor, respectively; a main controller that is connected to the power manager, monitors information about power supplied to the first sensor, second sensor, and third sensor, and integrally controls accelerating, braking, and shifting of the vehicle in response to the accelerator pedal output signal, brake pedal output signal, and transmission output signal; and a communicator that sends the accelerator pedal output signal, brake pedal output signal, and transmission output signal to a plurality of relevant control units.

An integrated control system of a vehicle includes: a power manager that receives power of a vehicle and supplies power to a first sensor, a second sensor, and a third sensor connected to an accelerator pedal, a brake pedal, and a transmission, respectively; a sensor signal receiver that receives an accelerator pedal output signal, a brake pedal output signal, and a transmission output signal from the first sensor, second sensor, and third sensor, respectively; a main controller that is connected to the power manager, monitors information about power supplied to the first sensor, second sensor, and third sensor, and integrally controls accelerating, braking, and shifting of the vehicle in response to the accelerator pedal output signal, brake pedal output signal, and transmission output signal; and a communicator that sends the accelerator pedal output signal, brake pedal output signal, and transmission output signal to a plurality of relevant control units.

A computer-implemented method comprises: continuously monitoring, by an assisted-driving (AD) system using a sensor, surroundings of a vehicle being controlled by a driver; detecting, by the AD system using the sensor, a parking spot that is available; planning, by the AD system and in response to detecting the parking spot, a trajectory for the vehicle to park in the parking spot; and generating a prompt to the driver, by the AD system and in response to detecting the parking spot, to have the AD system handle parking of the vehicle in the parking spot, the prompt performed before the vehicle reaches the parking spot.

A computer-implemented method comprises: continuously monitoring, by an assisted-driving (AD) system using a sensor, surroundings of a vehicle being controlled by a driver; detecting, by the AD system using the sensor, a parking spot that is available; planning, by the AD system and in response to detecting the parking spot, a trajectory for the vehicle to park in the parking spot; and generating a prompt to the driver, by the AD system and in response to detecting the parking spot, to have the AD system handle parking of the vehicle in the parking spot, the prompt performed before the vehicle reaches the parking spot.

An information processing apparatus detects occurrence of a request to switch to manual driving during autonomous driving control of a first vehicle, acquires an utterance of a driver in a case where there is occurrence of the request to switch to manual driving, and presents, to the driver, a part of an explanation about a reason for occurrence of the request to switch to manual driving according to the utterance of the driver.

An information processing apparatus detects occurrence of a request to switch to manual driving during autonomous driving control of a first vehicle, acquires an utterance of a driver in a case where there is occurrence of the request to switch to manual driving, and presents, to the driver, a part of an explanation about a reason for occurrence of the request to switch to manual driving according to the utterance of the driver.

A driver assistance system for automated longitudinal guidance of a motor vehicle configured to detect at least one turning opportunity for the motor vehicle, and to reduce the speed of the motor vehicle in accordance with the at least one detected turning opportunity.

A driver assistance system for automated longitudinal guidance of a motor vehicle configured to detect at least one turning opportunity for the motor vehicle, and to reduce the speed of the motor vehicle in accordance with the at least one detected turning opportunity.

A personalized adaptive cruise control (P-ACC) system and associated algorithm are disclosed for determining a driver's preferred following gap in relation to vehicle speed based on periods of steady-state operation of a vehicle. While the P-ACC system is activated, vehicle transition states initiated by driver manual interventions such as takeover or overwrite events are used to identify subsequent periods of vehicle steady-state operation. Vehicle dynamics data captured during periods of steady-state operation is stored as steady-state data, which is then used to train a machine learning model to learn the driver's preferred following gap. This learned relationship is fed into second-order vehicle dynamics to determine a target acceleration for achieving the desired following gap while the P-ACC system is activated. Upon achieving the desired following gap, the vehicle speed may be held constant to maintain the following gap unless a change in lead vehicle speed necessitates updating the following gap.

A personalized adaptive cruise control (P-ACC) system and associated algorithm are disclosed for determining a driver's preferred following gap in relation to vehicle speed based on periods of steady-state operation of a vehicle. While the P-ACC system is activated, vehicle transition states initiated by driver manual interventions such as takeover or overwrite events are used to identify subsequent periods of vehicle steady-state operation. Vehicle dynamics data captured during periods of steady-state operation is stored as steady-state data, which is then used to train a machine learning model to learn the driver's preferred following gap. This learned relationship is fed into second-order vehicle dynamics to determine a target acceleration for achieving the desired following gap while the P-ACC system is activated. Upon achieving the desired following gap, the vehicle speed may be held constant to maintain the following gap unless a change in lead vehicle speed necessitates updating the following gap.