A driving assistance method for a motor vehicle (1) when approaching a speed bump comprises, according to the invention: detecting and tracking at least one other moving vehicle (4.sub.1) in front of the motor vehicle (1) based on processing images captured by a camera (10) on board the motor vehicle (1); establishing a temporal profile of the estimated distance between the motor vehicle (1) and the at least one detected and followed other moving vehicle (4.sub.1); detecting an anomaly area in the temporal profile; and estimating a distance d.sub.bump between the motor vehicle (1) and a speed bump (3) on the basis of the estimated distance between the motor vehicle (1) and the at least one other vehicle (4.sub.1) at a time separate from the times corresponding to the detected anomaly area.

A drive mode switch control device acquires operation information. The drive mode switch control device switches a drive state among at least an autonomous drive state, a manual drive state, and a coordination drive state. The operation detection unit detects a first operation and a second operation based on the operation information when the drive state is not in the manual drive state. The second operation is the drive operation different from the first operation and input after the input of the first operation. The drive mode switch control device switches the drive state from the autonomous drive state to the coordination drive state based on a detection determination of the first operation. The drive mode switch control device switches the drive state from the coordination drive state to the manual drive state based on a detection determination of the first operation.

To correct an axis deviation of a sensor. An aiming device, which calculates correction amounts of detection results of two or more sensors using the detection results of the sensors, includes: a sensor coordinate conversion unit that converts sensor data detected by the sensor from a coordinate system unique to the sensor into a predetermined unified coordinate system; a target selection unit that selects predetermined features from the sensor data detected by each of the sensors; a function fitting unit that defines functions each approximating an array state of the selected features for the respective sensors; a fitting result comparison unit that compares the functions each approximating the array state of the features detected by each of the sensors; and a correction value calculation unit that calculates a correction amount for converting coordinates of the features detected by the sensors from a result of the comparison of the functions.

A drive assistance device includes an automatic brake unit configured to perform automatic brake control, a brake hold unit configured to perform a brake hold control keeping the vehicle stopped, a brake hold cancel unit configured to cancel the brake hold control when it is determined that a predetermined cancel condition is satisfied, a surroundings information obtaining unit configured to obtain surroundings information indicating a situation around the vehicle, a maneuver information obtaining unit configured to obtain maneuver information about a driving maneuver performed by a driver of the vehicle, a maneuver determination unit configured to determine, based on the surroundings information and the maneuver information whether the driving maneuver performed during the brake hold control is appropriate for the situation around the vehicle, and a prohibition unit configured to prohibit cancelling the brake hold control as long as it is determined that the driving maneuver is inappropriate.

A driving assistance method includes: detecting a first other vehicle entering an intersection on a first route where a host vehicle is traveling from a second route; predicting whether or not the first other vehicle will stop in the intersection, and predicting a stop position of the first other vehicle; calculating a minimum distance of a first gap between a vehicle body of the first other vehicle and a surrounding object around the first other vehicle or between the vehicle body of the first other vehicle and a road edge of a travel lane of the first other vehicle when the first other vehicle stops at the predicted stop position; and predicting according to the calculated minimum distance whether or not a second other vehicle, which is a following vehicle behind the first other vehicle, may slip through the first gap from behind the first other vehicle.

A travel control method for a vehicle includes: presenting a vehicle traveling on a route to a preliminarily set destination with overtaking information as to whether to accept execution of an overtaking assist function for overtaking a preceding vehicle by changing lanes through autonomous travel control; and executing the overtaking assist function when an acceptance input of accepting the execution of the overtaking assist function is detected in response to presentation of the overtaking information. This method further includes: detecting a position of the vehicle on the route; and detecting whether or not there is a traveling direction change point on the route ahead of the position of the vehicle, a point at which the vehicle must change its traveling direction. When the distance from the vehicle to the traveling direction change point is shorter than a preliminarily set distance, the presentation of the overtaking information is not performed.

A driving support apparatus includes a memory, and a hardware processor coupled to the memory. The hardware processor is configured to detect an obstacle; control a driving force of the vehicle to perform collision avoidance control for the obstacle; calculate a reduction amount of the driving force when the vehicle has climbed over a target object, in accordance with a distance between the vehicle and the obstacle when the vehicle has climbed over the target object; and control the collision avoidance control so as to gradually increase the driving force from an initial driving force being a driving force smaller than required driving force determined from an accelerator position of an accelerator pedal until a vehicle speed of the vehicle reaches a set vehicle speed, and so as to reduce the driving force by the reduction amount when the vehicle speed of the vehicle reaches the set vehicle speed.

An accident liability tracking system includes a processing device programmed to receive, from an image capture device, an image representative of an environment of the host vehicle, to analyze the image to identify a target vehicle in the environment of the host vehicle, and determine one or more characteristics of a navigational state of the target vehicle. The device is further programmed to compare the characteristics of the navigational state of the target vehicle to at least one accident liability rule, store one or more values indicative of potential accident liability on the part of the identified target vehicle based on the comparison of the characteristics of the navigational state of the identified target vehicle to the at least one accident liability rule, and output the one or more values, after an accident between the host vehicle and a target vehicle, for determining liability for the accident.

A method for assisting in the driving of a vehicle on a fast road with carriageways separated by a safety rail in which the presence of the safety rail is detected is disclosed. The safety rail is modelled from measurements performed continuously by at least one laser scanner sensor mounted on the motor vehicle, with the determination of a confidence index associated with the detection by the laser scanner sensor, an automatic driving mode is activated when the confidence index I.sub.CONF is above a confidence threshold. This mode is maintained as long as a current confidence index associated with the detection is above the confidence threshold, and this mode is deactivated when the current confidence index passes below said confidence threshold. The density of traffic in front of the motor vehicle is estimated from images captured by an embedded camera.

A real-world vehicle includes multiple data sources that generate sensor data that is spatially-mapped to a real-world region; a data fusion system is configured to fuse or integrate (i) the spatially-mapped sensor data with (ii) virtual data, that has been generated outside of the vehicle or generated independently of the operation of the vehicle, and is spatially-mapped to a virtual world. This enables a fusion of the real and virtual worlds which enables a self-driving car to interact not only with the physical world but also to virtual objects introduced into the path of the car (e.g. by a test or development engineer) to test how well the car and its autonomous driving systems cope with the virtual object.