An autonomous vehicle includes an array of sensors, a processor, and a switch. The array of sensors generate sensor data related to one or more objects in an external environment of the autonomous vehicle and the processor determines an environmental context. The switch transfers the sensor data from the array of sensors to the processor, where the switch is configured to: (a) receive first sensor data from a first sensor group of the array of sensors; (b) receive second sensor data from a second sensor group of the array of sensors; (c) determine an order of transmission of the first sensor data over the second sensor data in response to the environmental context; and (d) transmit the first sensor data to the processor prior to transmitting the second sensor data based on the order of transmission.

A method for predicting an ego-lane for a vehicle. The method includes: receiving at least one image captured by at last one camera sensor of the vehicle, which depicts a lane that may be used by a vehicle; ascertaining a center line of the lane, which extends through a center of the lane, by implementing a trained neural network on the captured image, the neural network being trained via regression to ascertain a center line of a lane, which extends in a center of the lane, based on captured images of the lane; outputting a plurality of parameters, which describe the center line of the lane, via the neural network; generating the center line based on the parameters of the center line; identifying the center line of the lane as the ego-lane of the vehicle; and providing the ego-lane.

A method for operating a transverse stabilization system of a vehicle. A steering direction of the vehicle and a setpoint direction of the vehicle are read in, with a transverse stabilization target for the transverse stabilization system being determined using the steering direction and the setpoint direction.

An adaptive cruise control system includes an information acquisition unit having a main detector and a secondary detector, a control unit, and an execution unit. The main detector detects an object located ahead of the vehicle. The control unit determines whether control of the vehicle is required depending on an actual value determined by the main detector and the threshold value of a system property characterizing the driving environment. The execution unit controls the vehicle. The secondary detector is arranged such that its field of view for detecting an object located at an angle ahead of the vehicle covers the boundary of the main detector's field of view, and which is oriented outwards in relation to the forward direction of the vehicle. The secondary detector sends an indication signal to adjust the threshold value of the system property when an object is detected.

A system for receiving user input from an internal vehicle component surface includes a flat surface layer of the internal vehicle component that includes a first portion made of an elastic material and a second portion that surrounds the first portion, and a push-button assembly located beneath the first portion of the flat surface layer. The push-button assembly includes a push-button switch that is switched into at least a first switching state by downward pressure, and a vertical movement mechanism that when activated causes the push-button switch to move vertically in a direction of the flat surface layer. Vertical movement of the push-button switch causes a vertical displacement of the first portion of the flat surface layer, and downward pressure on the first portion of the flat surface layer when vertically displaced causes a corresponding downward pressure to the push-button switch, switching the push-button switch into the first switch state.

A system and method for slowing a vehicle. Road conditions around the vehicle are monitored, and determined if those road conditions are hazardous. An engine control unit is informed of the hazardous road conditions and alters the operation of the engine control unit in response to the hazardous road conditions. When an operator of the vehicle desires to slow the vehicle down, an indication is received indicating the intent to slow the vehicle down. The vehicle is then slowed based upon the altered operation of the engine control unit by applying a vacuum to increase a manifold vacuum of the engine.

The described aspects and implementations enable fast and accurate verification of radar detection of objects in autonomous vehicle (AV) applications using combined processing of radar data and camera images. In one implementation, disclosed is a method and a system to perform the method that includes obtaining a radar data characterizing intensity of radar reflections from an environment of the AV, identifying, based on the radar data, a candidate object, obtaining a camera image depicting a region where the candidate object is located, and processing the radar data and the camera image using one or more machine-learning models to obtain a classification measure representing a likelihood that the candidate object is a real object.

An autonomous robotic vehicle is capable of detecting, identifying, and locating the source of gas leaks such as methane. Because of the number of operating components within the vehicle, it may also be considered a robotic system. The robotic vehicle can be remotely operated or can move autonomously within a jobsite. The vehicle selectively deploys a source detection device that precisely locates the source of a leak. The vehicle relays data to stakeholders and remains powered that enables operation of the vehicle over an extended period. Monitoring and control of the vehicle is enabled through a software interface viewable to a user on a mobile communications device or personal computer.

A behavior prediction system predicts human behaviors based on environment-aware information such as camera movement data and geospatial data. The system receives sensor data of a vehicle reflecting a state of the vehicle at a given time and a given location. The system determines a field of concern in images of a video stream and determines one or more portions of images of the video stream that correspond to the field of concern. The system may apply different levels of processing powers to objects in the images based on whether an object is in the field of concern. The system then generates features of objects and identify VRUs from the objects of the video stream. For the identified VRUs, the system inputs a representation of the VRUs and the features into a machine learning model, and outputs from the machine learning model a behavioral risk assessment of the VRUs.

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.