An autonomous vehicle includes a control system, an array of sensors, processing logic, and a switch. The processing logic generates operation instructions based on sensor data and the control system controls the autonomous vehicle based on the operation instructions. The array of sensors generate the sensor data that is related to objects in an external environment. The switch is coupled between the sensors and the processing logic to buffer the processing logic from the sensor data. The switch is further coupled between the processing logic and the control system to provide the operation instructions from the processing logic to the control system. The switch includes a prioritization engine that prioritizes an order of transmission, from the switch to the processing logic, of the first sensor data over the second sensor data based on received vehicle operation data.

Systems and methods for vehicle lane change control are described. Some implementations may include determining a kinematic state of a vehicle moving in an origin lane; detecting, based on data from one or more sensors of the vehicle, objects that are moving in a target lane of the road; determining a headway constraint in terms of a preparation time, a preparation acceleration to be applied to the vehicle during the preparation time, and an execution time during which the vehicle is to transition from the origin lane to the target lane; determining values of the preparation time, the execution time, and the preparation acceleration subject to a set of constraints including the headway constraint; and determining a motion plan that will transition the vehicle from the origin lane to the target lane based at least in part on the preparation time, the execution time, and the preparation acceleration.

Methods and systems for monitoring use and determining risks associated with operation of a vehicle having one or more autonomous operation features are provided. According to certain aspects, operating data may be recorded during operation of the vehicle. This may include information regarding the vehicle, the vehicle environment, use of the autonomous operation features, and/or control decisions made by the features. The control decisions may include actions the feature would have taken to control the vehicle, but which were not taken because a vehicle operator was controlling the relevant aspect of vehicle operation at the time. The operating data may be recorded in a log, which may then be used to determine risk levels associated with vehicle operation based upon risk levels associated with the autonomous operation features. The risk levels may further be used to adjust an insurance policy associated with the vehicle.

A parking control system for an autonomous vehicle is provided. The parking control system includes a parking control device configured to monitor a location and movement of an autonomous vehicle which enters a parking lot, based on a 3D electronic map, calculate a driving trajectory to a parking space selected by a driver of the autonomous vehicle based on sensor data collected from various sensor in the parking lot and vehicle information received from the autonomous vehicle, and provide information about the calculated driving trajectory to the autonomous vehicle. The autonomous vehicle travels to the parking space based on the driving trajectory received from the parking control device and parks in the parking space.

A method for setting a tuning parameter for an Automated Driving System (ADS) of a vehicle is disclosed. A corresponding non-transitory computer-readable storage medium, vehicle control device and a vehicle comprising such a control device are also disclosed. The method comprises receiving environmental data from a perception system of the vehicle, said environmental data comprising a plurality of environmental parameters, determining, by means of a self-learning model, an environmental scenario based on the received environmental data; setting the tuning parameter for the ADS based on the self-learning model and the determined environmental scenario, the tuning parameter defining a dynamic parameter of the ADS, receiving at least one signal representative of a vehicle user feedback on the set tuning parameter, and updating the self-learning model for the set tuning parameter for the identified environmental scenario based on the received vehicle user feedback.

Technologies disclosed relate to a remote intervention system for the operation of a vehicle, which can be an autonomous vehicle, a vehicle that includes driver assist features, a vehicle used for ride sharing services or the like. The system includes a vehicle sending a request for remote intervention to a remote operator when the operation of the vehicle is suspended. The request for remote intervention can include a request for object identification or a request for decision confirmation. The vehicle can update vehicle operation based in part on vehicle-based sensor data and a response to the remote intervention request from the remote operator. The remote operator can be a human operator or an AI operator.

In an apparatus for determining a traveling position of an own vehicle that is an autonomous driving vehicle equipped with the apparatus, a judgment section is configured to judge presence or absence of at least one of a travel history of other vehicles regarding the lane in which the own vehicle is traveling and an object that is located in the vicinity of the own vehicle within the lane in which the own vehicle is traveling and should be avoided coming into contact with. The traveling position is a widthwise position of the own vehicle within a lane in which the own vehicle is traveling. A determination section is configured to determine the traveling position using the travel history in response to the judgment section judging that the travel history exists and using position information of the object in response to the judgment section judging that the object exists.

An ADAS-linked active hood apparatus includes an ADAS device that measures information regarding a driving state of a vehicle and an object and a collision sensor unit that is positioned at a front of the vehicle and measures collision with the object. An active hood lift system (AHLS) raises one end of a hood of the vehicle based on a signal from the collision sensor unit. A controller sets a pedestrian detection threshold (PDT) turn, receives information regarding a plurality of front objects from the ADAS device to compensate for a PDT, compensates for an output reference value of the collision sensor unit based on the compensated PDT, and determines whether collision occurs using the collision sensor unit to adjust pop-up of the AHLS when an output value equal to or greater than the compensated reference value is applied.

A vehicle assistance system for a vehicle is provided. A corresponding computer implemented method and computer program product are also provided.

A vehicle driver assist system includes an expert evaluation system to fuse information acquired from various data sources. The data sources can correspond to conditions associated with the vehicle as a unit as well as external elements. The expert evaluation system monitors and evaluates the information from the data sources according to a set of rules by converting each data value into a metric value, determining a weight for each metric, assigning the determined weight to the metric, and generating a weighted metric corresponding to each data value. The expert evaluation system compares each weighted metric (or a linear combination of metrics) against one or more thresholds. The results from the comparison provide an estimation of a likelihood of one or more traffic features occurring.