A system, method, and processor-readable medium for assessing the reliability of vehicle systems used in an autonomous vehicle. The assessment may be performed at least in part on the basis of data collected by one or more of the vehicle's sensors. The result of the assessment may be used as the basis for decisions about vehicle operation carried out by an autonomous driving module.

Adaptive cruise control systems and methods of providing overtake acceleration aid in host vehicles. In one embodiment, the adaptive cruise control system includes an ultrasonic sensor, a radar sensor, a camera, and an electronic controller. The ultrasonic sensor, the radar sensor, and the camera are configured to sense within fields-of-view of a neighboring lane. Responsive to receiving an input indicating an overtake request, the electronic controller is configured determine when an object is located in the neighboring lane based in part on first data received from the ultrasonic sensor. The electronic controller is also configured to determine a velocity of the object when the object is located within the field-of-view of the radar sensor or the camera. The electronic controller is further configured to determine an overtake acceleration boost based in part on the velocity of the object and apply the overtake acceleration boost to the host vehicle.

Described herein are technologies relating to controlling an autonomous vehicle based upon a computed lane boundary. The computed lane boundary defines a travel path for the autonomous vehicle when the autonomous vehicle turns across a lane of traffic at an intersection of two or more roadways. The autonomous vehicle navigates turn based upon the computed lane boundary, which allows the autonomous vehicle to enter the intersection without crossing into a lane of oncoming traffic.

According to an aspect of an embodiment, operations may comprise accessing a set of vehicle poses of one or more vehicles; for each of the set of vehicle poses, accessing a high definition (HD) map of a geographical region surrounding the vehicle pose, with the HD map comprising a three-dimensional (3D) representation of the geographical region, determining a measure of constrainedness for the vehicle pose, with the measure of constrainedness representing a confidence for performing localization for the vehicle pose based on 3D structures surrounding the vehicle pose, and storing the measure of constrainedness for the vehicle pose; and for each of the geographical regions surrounding each of the set of vehicle poses, determining a measure of constrainedness for the geographical region based on measures of constrainedness of vehicle poses within the geographical region, and storing the measure of constrainedness for the geographical region.

Embodiments of the disclosure provide systems and methods for ballistically estimating vehicle data. The system may include a communication interface configured to receive a first vehicle measurement taken at a first time point and a second vehicle measurement taken at a second time point. The system may further include at least one processor. The at least one processor may be configured to estimate a first version of vehicle data at a first speed for each of the second time point and a plurality of intermediate time points between the first time point and the second time point based on the first vehicle measurement using a prediction model. The at least one processor may be further configured to compute a second version of vehicle data at a second speed for the second time point based on the second vehicle measurement. The first speed is faster than the second speed. The at least one processor may also be configured to determine whether to update the prediction model based on a comparison between the first version of vehicle data and the second version of vehicle data for the second time point.

Systems and methods of detecting a traffic object outside of a vehicle and controlling the vehicle. The systems and methods receive perception data from a sensor system included in the vehicle, determine a focused Region Of Interest (ROI) in the perception data, scale the perception data of the at least one focused ROI, process the scaled perception data of the focused ROI using a neural network (NN)-based traffic object detection algorithm to provide traffic object detection data, and control at least one vehicle feature based, in part, on the traffic object detection data.

The present application discloses a method and an apparatus for processing map data, which relate to autonomous driving technologies in the field of data processing. The specific implementation is that: a controlling unit inputs initial positioning data collected by a data collecting unit to a data fusing unit to obtain fused positioning data, where the initial positioning data and the fused positioning data are data in a first coordinate system; the controlling unit obtains target positioning data according to the fused positioning data, where the target positioning data is data in a second coordinate system, and the second coordinate system is a coordinate system obtained by offsetting the first coordinate system; and the controlling unit performs a positioning operation on the target positioning data through at least one positioning unit, to determine a position of a vehicle corresponding to an autonomous driving system.

The technology relates to detection of aberrant driving situations during operation of a vehicle in an autonomous driving mode. Aberrant situations may include potential theft or unsafe conditions, which are determined according to one or more signals. The signals are derived from information detected about the environment around the vehicle, such as from one or more sensors disposed on the vehicle. In response to an aberrant situation, the vehicle may take various corrective action, such as rerouting, locking down the vehicle or communicating with remote assistance. The type of corrective action taken may depend on a type of cargo being transported or whether one or more passengers are in the vehicle. If there are passengers, the system may communicate with the passengers via the passenger's client computing devices or by presenting visual or audible information via a user interface system of the vehicle.

Methods and systems for autonomous vehicle recharging or refueling are disclosed. Autonomous electric vehicles may be automatically recharged by routing the vehicles to available charging stations when not in operation, according to methods described herein. A charge level of the battery of an autonomous electric vehicle may be monitored until it reaches a recharging threshold, at which point an on-board computer may generate a predicted use profile for the vehicle. Based upon the predicted use profile, a time and location for the vehicle to recharge may be determined. In some embodiments, the vehicle may be controlled to automatically travel to a charging station, recharge the battery, and return to its starting location in order to recharge when not in use.

Methods and systems for assessing, detecting, and responding to malfunctions involving components of autonomous vehicle and/or smart homes are described herein. Autonomous operation features and related components can be assessed using direct or indirect data regarding operation. Such assessment may be performed to determine the condition of components for salvage following a collision or other loss-event. To this end, the information regarding a plurality of components may be received. A component of the plurality of components may be identified for assessment. Assessment may including causing test signals to be sent to the identified component. In response to the test signal, one or more responses may be received. The received response may be compared to an expected response to determine whether the identified component is salvageable.