A method for determining a speed profile to be followed by a vehicle, including acquiring event data including a distance from an event and a target speed at this event for the vehicle, and determining a speed profile to be followed as a function of time, between an initial speed and the target speed in three successive distinct phases, respectively a first phase in which the jerk is set constant at a predetermined maximum jerk value to reach an optimal target acceleration value, a second phase in which the optimal target acceleration value is kept constant, and a third phase in which the jerk is again set constant to reach a zero acceleration value at the end of the third phase. The optimal target acceleration value is such that the distance required to carry out the three phases of the profile is equal to the distance from the event.

Systems and methods for predicting component failure in a vehicle and alerting a driver based thereon. In an embodiment, the method includes receiving sensor data from at least one vehicle sensor over a period of time, processing the sensor data using a predictive model to detect an anomaly indicative of an upcoming component failure, determining a severity of the upcoming component failure based on at least one operating characteristic of the vehicle, and providing an alert to the driver of the vehicle regarding the severity of the upcoming component failure.

To provide a vehicle control system that is capable of planning a trajectory that can ensure more visibility and enables safe traveling when an invisible range of a sensor exists.

A vehicle control system that plans a target trajectory of a vehicle based on recognition information from an external environment sensor, the vehicle control system including a recognizing unit that recognizes an object at a periphery of the vehicle based on the recognition information; and a trajectory planning unit that plans the target trajectory such that an actual detection range of the external environment sensor becomes wide when the recognizing unit recognizes the object.

A vehicle control system (500) controls a vehicle whereon a plurality of ECUs (30) and a vehicle integrated control device (10) to control the plurality of ECUs (30) are mounted. The vehicle integrated control device (10) includes a control target value operation unit to calculate a control target value to control the plurality of ECUs (30). Further, the vehicle integrated control device (10) includes a prediction control value operation unit to estimate a state of the vehicle in the future, and to calculate a prediction control value to control the plurality of ECUs (30). The vehicle integrated control device (10) includes an instruction signal generation unit to generate an instruction signal including an operation instruction and a prediction control instruction. Each of the plurality of ECUs (30) includes an actuator control unit to control an actuator (50) based on the prediction control instruction.

A device and a method for controlling autonomous driving control a speed of an autonomous vehicle before downhill travel. The device and method calculate a travel resistance of an autonomous vehicle on a travel-intended-route, including a downhill route, a main braking pressure required to travel at a constant speed, and a brake temperature based on braking. The device and method determine whether to reduce the main braking pressure based on the calculated brake temperature and calculates a decreased amount of the main braking pressure and an increased amount of a speed of the autonomous vehicle based on the decreased amount of the main braking pressure on the travel-intended-route when determining to reduce the main braking pressure. The device and method limit a maximum speed of the autonomous vehicle before entering the travel-intended-route based on the increased speed amount.

A vehicle trajectory prediction device is provided. The vehicle trajectory prediction device includes a transceiver, at least one processor, and at least one memory operatively connected with the at least one processor to store at least one instruction causing the at least one processor to perform operations. The operations receive first trajectory data for an ego-vehicle and second trajectory data for at least one neighbor-vehicle, obtain a first feature vector from a first extractor and obtain a second feature vector from a second extractor, obtain an interdependency feature vector between the ego-vehicle and the at least one neighbor-vehicle from a third extractor having mapping data generated by mapping the second feature vector to the second trajectory data as input data, and generate predicted trajectory data of the ego-vehicle from a trajectory generator having the first feature vector and the interdependency feature vector as input data.

A speed control system (12) for a vehicle (100), the speed control system (12) being configured to: automatically cause application of positive and negative torque, as required, to one or more wheels of a vehicle (100) to cause a vehicle to travel in accordance with a target speed value, the target speed value being stored in a memory of the control system (12); and detect a crest of a slope ahead of the vehicle (100); wherein the speed control system (12) is configured automatically to attempt to adjust a speed of the vehicle (100) to cause the vehicle (100) to travel at a predetermined crest speed value when a crest of a slope is detected ahead of the vehicle (100), the predetermined crest speed value being determined in dependence at least in part on terrain gradient information respect of terrain prior to the crest.

Provided is a sensor recognition integration device capable of reducing the load of integration processing so as to satisfy the minimum necessary accuracy required for vehicle travel control, and capable of improving processing performance of an ECU and suppressing an increase in cost. A sensor recognition integration device B006 that integrates a plurality of pieces of object information related to an object around an own vehicle detected by a plurality of external recognition sensors includes: a prediction update unit 100 that generates predicted object information obtained by predicting an action of the object; an association unit 101 that calculates a relationship between the predicted object information and the plurality of pieces of object information; an integration processing mode determination unit 102 that switches an integration processing mode for determining a method of integrating the plurality of pieces of object information on the basis of a positional relationship between a specific region (for example, a boundary portion) in an overlapping region of detection regions of the plurality of external recognition sensors and the predicted object information; and an integration target information generation unit 104 that integrates the plurality of pieces of object information associated with the predicted object information on the basis of the integration processing mode.

A method of controlling movement of multi-vehicle includes acquiring a constraint condition under which vehicles move and a calculation cycle for calculating movement routes of the vehicles; acquiring a position of each vehicle; specifying a target position for each vehicle; calculating, based on the position of each vehicle, the target position, and the constraint condition, a movement route for prediction steps of each vehicle; determining, based on the movement routes of the vehicles, a driving condition of each vehicle from a current time to a unit time; and controlling movement of each vehicle. Calculating the movement route including performing optimization calculation based on an evaluation function, evaluation of which becomes higher as a deviation between the vehicle and the target position for each prediction step becomes smaller, and the constraint condition, to calculate the movement route.

Provided are methods for vehicle state estimation based on sensor data, which can include receiving the sensor data generated by one or more sensors, calculating a cornering stiffness value associated with the vehicle, predicting a lateral velocity value associated with the vehicle based on the cornering stiffness value, and outputting a set of vehicle state variables indicative of a current state of the vehicle at least by inputting the lateral velocity value into a recursive filter. Some methods described also include updating the cornering stiffness value based on the set of vehicle state variables, updating the lateral velocity value based on the updated cornering stiffness value, and updating the set of vehicle state variables based on the updated lateral velocity value. Systems and computer program products are also provided.