The disclosure relates to methods, systems, and apparatuses for virtual sensor data generation and more particularly relates to generation of virtual sensor data for training and testing models or algorithms to detect objects or obstacles, such as wheel stops or parking barriers. A method for generating virtual sensor data includes simulating a three-dimensional (3D) environment comprising one or more objects. The method includes generating virtual sensor data for a plurality of positions of one or more sensors within the 3D environment. The method includes determining virtual ground truth corresponding to each of the plurality of positions, wherein the ground truth includes information about at least one object within the virtual sensor data. The method also includes storing and associating the virtual sensor data and the virtual ground truth.

A passive vehicular heatstroke prevention system monitors carbon dioxide (CO.sub.2) and infrared (IR) energy levels to determine whether a child is present inside a closed vehicle, and, if so, monitors the temperature in the vehicle and, if the temperature in the vehicle exceeds at least one preset critical value, automatically lowers the temperature in the vehicle and contacts the driver/caregiver and/or emergency personnel. The system detects the presence of a child in the closed vehicle by detecting a critical level of carbon dioxide in the air within the vehicle, while monitoring the interior vehicle temperature and takes corrective action to prevent the temperature from exceeding a preset value, such as by activating the vehicle's air conditioning unit and lowering the vehicle's windows, as well as contacting the driver/caregiver and/or emergency personnel.

A live map system may be used to propagate observations collected by autonomous vehicles operating in an environment to other autonomous vehicles and thereby supplement a digital map used in the control of the autonomous vehicles. In addition, a live map system in some instances may be used to propagate location-based teleassist triggers to autonomous vehicles operating within an environment. A location-based teleassist trigger may be generated, for example, in association with a teleassist session conducted between an autonomous vehicle and a remote teleassist system proximate a particular location, and may be used to automatically trigger a teleassist session for another autonomous vehicle proximate that location and/or to propagate a suggested action to that other autonomous vehicle.

Methods and devices for managing output devices in a vehicle are disclosed. Data is obtained representing a sensed position of the first vehicle and a sensed feature in a proximity of the first vehicle. A first probability density function (PDF) is defined based on the obtained data, representing likelihood of a future position of the first vehicle. A second PDF is defined based on the obtained data, representing likelihood related to a proxemic risk presented by the sensed feature. A risk metric is computed representing a likelihood of the proxemic risk to the first vehicle based on an overlap between the first PDF and the second PDF. A useable number of information bits is determined, based on the risk metric. An application is controlled to provide output, via output device(s) of the first vehicle, within the useable number of information bits.

An example method includes (a) obtaining sensor data descriptive of an environment of an autonomous vehicle; (b) obtaining a plurality of travel way markers from map data descriptive of the environment; (c) determining, using a machine-learned object detection model and based on the sensor data, an association between one or more travel way markers of the plurality of travel way markers and an object in the environment; and (d) generating, using the machine-learned object detection model, an offset with respect to the one or more travel way markers of a spatial region of the environment associated with the object.

Example implementations may relate to an obstacle detection system. In particular, an example device may include a light emitter, a line-image sensor, and a controller that are mounted on a rotatable component. In an example embodiment, the line-image sensor may receive light signals emitted from the light emitter. The controller may be communicatively coupled to the light emitter and line-image sensor and configured to determine a multipath signal based on the time of flight of the light signal and the position along the line-image sensor at which the line-image sensor received the given reflected light signal.

A vehicle control device includes: a sensor configured to detect roll angular velocity of a vehicle body of a vehicle; and a controller configured to decelerate the vehicle by controlling at least one of a driving force source to drive the vehicle and a braking device to brake the vehicle during a period in which an absolute value of roll angular acceleration of the vehicle body increases and/or accelerate the vehicle by controlling the driving force source during a period in which an absolute value of the roll angular acceleration decreases, based on the roll angular velocity detected by the sensor.

Systems, methods, and other embodiments described herein relate to implementing and calibrating a learning model for inferring operator intent by estimating grip intensity. In one embodiment, a method includes estimating, using a learning model during a driving scenario, first grip intensity on a steering device for a vehicle according to initial image data depicting a hand of an operator gripping outside the set areas that have pressure sensors. The method also includes calibrating the learning model for the operator and the steering device using grip measurements and additional image data acquired from gripping inside the set areas. The method also includes computing, using the learning model during the driving scenario, second grip intensity outside the set areas on the steering device according to hand images acquired about the operator. The method also includes adapting a vehicle parameter of the vehicle according to the second grip intensity.

Control commands in a system and method for autonomously driving a vehicle or partially autonomously driving a vehicle are generated on the basis of an environment representation, which is generated from sensor signal of a sensing means. The environment representation where ambiguous objects are identified is such that information obtained from the driver of the vehicle are added. The system generates an information request. Additional information is extracted and accumulated in the environment representation map. Traffic is then determined and suitable control signals for the vehicles are generated. In case no ambiguous objects are included in the environment representation but the system is not capable of deciding on traffic, the driver is asked to disambiguate the situation or to instruct on dealing with the traffic.

Example implementations may relate to an obstacle detection system. In particular, an example device may include a light emitter, a line-image sensor, and a controller that are mounted on a rotatable component. In an example embodiment, the line-image sensor may receive light signals emitted from the light emitter. The controller may be communicatively coupled to the light emitter and line-image sensor and configured to determine a multipath signal based on the time of flight of the light signal and the position along the line-image sensor at which the line-image sensor received the given reflected light signal.