Among other things, we describe techniques for an interface board for harmonizing performance of different autonomous vehicles in a fleet. In an embodiment, the interface system includes a printed circuit board installed in a host vehicle that includes an interface controller and interface circuitry coupled to the interface controller. The interface circuitry includes a plurality of relays configured by the interface controller to route/pass sensor signals received from actuators of the host vehicle to the interface controller, and route/pass control signals from the interface controller to actuator controllers of the host vehicle that control the actuators in accordance with an operating mode. Signal conditioning circuitry in the interface controller conditions the sensor signals and control signals to ensure that the electrical characteristics of the sensor signals are compatible with the interface controller, and that the electrical characteristics of the control signals are compatible with the actuator controllers.

A method for processing friction data for vehicle tires on road segments, implemented by a processing system including at least one computer and an interface for remote communication with a plurality of vehicles, the method including: acquiring, from the plurality of vehicles, friction data for tires of the vehicles on a plurality of road segments, each friction datum including at least: a maximum coefficient of friction available to the vehicle on the road segment, and information relating to the road segment; establishing, for each road segment, a distribution of the friction data obtained from the plurality of vehicles for the road segment; and determining a plurality of road types, each road type comprising a set of road segments, from a measurement of similarity between the distributions of friction data obtained for each road segment.

A method for operating a vehicle in an automatic driving operation not requiring any user action which can be deactivated by a deactivation action of a driver of the vehicle includes, during the automatic driving operation in a learning phase, driving situations in which the driver deactivates the automatic driving operation are recorded by a surroundings recording device and the recorded driving situations are stored in a memory as subjectively critical driving situations. The method further includes, during an operating phase of the automatic driving operation, comparing a currently recorded driving situation to the stored subjectively critical driving situations and emitting a warning to the driver when the currently recorded driving situation matches one of the stored subjectively critical driving situations within a tolerance range.

An information processing server is configured to acquire target vehicle data having traveling conditions of target vehicles and position information of the target vehicles on a map, recognize, based on the target vehicle data, an unstable behavior position on the map where at least one of the target vehicles exhibits unstable behavior, measure an occurrence count of the unstable behavior positions within a predetermined period in a mesh preset on the map, enlarge the mesh to include the unstable behavior positions within the predetermined period such that the occurrence count of the unstable behavior positions is equal to or larger than a first threshold, and store the mesh and the unstable behavior positions of the mesh in a storage database related to each other.

The disclosure provides a method and device for generating driving suggestion, and computer-readable storage medium. The method comprises: acquiring N driving records, wherein the N driving records are derived from at least two vehicles, each driving record comprises a mapping relationship between a driving period and an acceleration value, and the N is an integer greater than 1; determining a plurality of acceleration metric values based on the acceleration values in the N driving records, wherein each vehicle corresponds to at least one acceleration metric value, and the acceleration metric value is positively correlated with the acceleration value; determining a metric threshold according to the plurality of acceleration metric values; and generating a driving suggestion based on the metric threshold and the driving record corresponding to any one of the at least two vehicles.

A method that may include receiving driving information and environmental metadata indicative of information sensed by the vehicle; detecting multiple driving events encountered during the driving over the path; determining driving events; for each driving event, determining a comfort based autonomous driving pattern information; for each driving event, determining an driving event identifier; and storing in at least one data structure a driving event identifier for each one of the multiple types of driving events, and a comfort based autonomous driving pattern information.

A storage device (301) stores a failure correspondence table (405) that indicates a countermeasure for a time when a failure occurs in an apparatus mounted on a vehicle (100). A failure countermeasure update unit (404) updates the countermeasure indicated in the failure correspondence table (405) in accordance with a change in a traveling environment of the vehicle (100).

An in-vehicle data collection and processing device receives real-time data from a plurality of sensors relating to at least one of a current vehicle condition and a current driver condition. The device then predicts, by processing at least a portion of the real-time data through a pre-trained pattern recognition algorithm, a likelihood of occurrence of at least one of a plurality of incidents involving the vehicle. In response, the device outputs one or more types of warnings and/or conducts a vehicle evasive maneuver if the likelihood is predicted to be above one or more thresholds.

An autonomous vehicle control system is provided. The control system may include a plurality of cameras to acquire a plurality of images of an area in a vicinity of a vehicle; and at least one processing device configured to: recognize a curve to be navigated based on map data and vehicle position information; determine an initial target velocity for the vehicle based on at least one characteristic of the curve as reflected in the map data; adjust a velocity of the vehicle to the initial target velocity; determine, based on the plurality of images, observed characteristics of the curve; determine an updated target velocity based on the observed characteristics of the curve; and adjust the velocity of the vehicle to the updated target velocity.

A vehicle control device includes a travel environment recognition device configured to recognize travel environment, a vehicle travel state detection device, and an autonomous/manual driving mode controller. The autonomous/manual driving mode controller includes a learning correction unit configured to store at least one of a plurality of control parameters indicating the vehicle travel state by operation of the driver in the autonomous driving mode, and to correct the control parameter in the autonomous driving mode according to the stored control parameter. When the stored control parameter is the control parameter changed by an operation by the driver midway in passing or after passing, the learning correction unit is configured to correct the control parameter in the autonomous driving mode so that an acceleration level or the deceleration level is increased midway in passing or after passing.