This disclosure is generally directed to systems and methods for protecting the health of occupants of a vehicle. In an example method, a sensor in a vehicle sends sensor data to a computer, based on detecting a symptomatic feature that indicates a health status of a first occupant of the vehicle. In one case, the symptomatic feature may be an elevated body temperature that may be symptomatic of a fever. In another case, the detected symptomatic feature may be a sound emitted by the first occupant such as, for example, a cough, a sneeze, or a wheeze. The computer evaluates the sensor data and identifies a health risk posed by the first occupant to a second occupant of the vehicle. The computer may address the health risk, for example, by modifying an air quality inside the vehicle or by instructing the occupants of the vehicle to don masks.

Embodiments of the invention include autonomous vehicles and mm-wave systems for communication between components. In an embodiment the vehicle includes an electronic control unit (ECU). The ECU may include a printed circuit board (PCB) and a CPU die packaged on a CPU packaging substrate. In an embodiment, the CPU packaging substrate is electrically coupled to the PCB. The ECU may also include an external predefined interface electrically coupled to the CPU die. In an embodiment, an active mm-wave interconnect may include a dielectric waveguide, and a first connector coupled to a first end of the dielectric waveguide. In an embodiment, the first connector comprises a first mm-wave engine, and the first connector is electrically coupled to the external predefined interface. Embodiments may also include a second connector coupled to a second end of the dielectric waveguide, wherein the second connector comprises a second mm-wave engine.

A method for the automatic control of the longitudinal dynamics of a vehicle is provided by which vehicles traveling ahead are detected. If an upcoming traffic jam is detected, the vehicle is decelerated until a predefined distance behind the tail end of the traffic jam is reached. When the predefined distance from the traffic jam tail end has been reached, the vehicle automatically controlled in its longitudinal dynamics is able to close the remaining, predefined distance to the traffic jam tail end at a low differential velocity in comparison to the velocity of the traffic jam tail end. Using an additional rear sensor system that senses trailing vehicles, the controlled vehicle is made to close the distance to the traffic jam tail end only if a trailing vehicle was detected.

Disclosed is a device for identifying sirens of priority vehicles and for warning a hearing-impaired driver of a motor vehicle of a presence of at least one priority vehicle in an environment of his vehicle, including at least one sound receiver, a computing unit for analyzing the audio recording and a unit for comparing the frequencies of the audio recording with frequencies previously stored in storage of the computing unit as being associated with a siren of a given priority vehicle, and for doing so for each type of priority vehicle, and at least one visual and/or vibratory element for warning the driver when a presence of a priority vehicle is detected in an environment of the motor vehicle.

An active impact control system includes: at least one actuator couplable to a crush structure of a vehicle and couplable to a portion of a structure of the vehicle; at least one sensor configured to sense impact with an object; and a controller configured to receive information from the at least one sensor and to determine a location and angle of impact based on the information received from the sensor, the controller being further configured to selectively signal the actuator to cause the crush structure to move relative to the structure of the vehicle.

The technology relates to using on-board sensor data, off-board information and a deep learning model to classify road wemess and/or to perform a regression analysis on road wetness based on a set of input information. Such information includes on-board and/or off-board signals obtained from one or more sources including on-board perception sensors, other on-board modules. external weather measurement, external weather services, etc. The ground truth includes measurements of water film thickness and/or ice coverage on road surfaces. The ground truth, on-board and off-board signals are used to build the model. The constructed model can be deployed in autonomous vehicles for classifying/regressing the road wetness with on-board and/or off-board signals as the input, without referring to the ground truth. The model can be applied in a variety of ways to enhance autonomous vehicle operation, for instance by altering current driving actions, modifying planned routes or trajectories, activating on-board cleaning systems, etc.

The present disclosure provides a method, system, and apparatus for processing parking and a vehicle controller, and relates to the field of intelligent transportation technology, specifically to the field of automated parking technology. The method is executed by a parking system deployed on a vehicle controller, the parking system including a perception module and other modules except the perception module; the perception module being deployed on a first operating system in the vehicle controller; and the other modules being deployed on a second operating system in the vehicle controller; the method includes: processing an image collected by an image collector through the perception module to obtain perception result data; and controlling a vehicle based on the perception result data obtained from the perception module by the other modules.

Systems and methods for generating images from an autonomous vehicle ride. Systems and methods are provided for determining whether a vehicle route passes a photogenic location and, if so, capturing an image at the photogenic location. The user can be provided with an option to take a detour to a nearby photogenic location. Additionally, a route can be selected based on one or more photogenic locations along the route. Captured images are provided to the user.

Embodiments described herein provide a method for using one or more audio signals from one or more sensors to establish the presence and severity of precipitation at a particular location. Methods may include: receiving at least one first audio signal from a first audio sensor of a vehicle; extracting acoustical features including frequency and amplitude from the at least one first audio signal; receiving at least one second audio signal from a second audio sensor of the vehicle; extracting acoustical features including frequency and amplitude from the at least one second audio signal; processing the frequency and amplitude from the at least one first audio signal and the frequency and amplitude from the at least one second audio signal as inputs to an algorithm to generate an output from the algorithm; and determining, from the output of the algorithm, a precipitation condition and a confidence measure of the precipitation condition.

A method detects presence of a multi-tone siren type in an acoustic signal. The multi-tone siren type is associated with one or more siren patterns, where each siren pattern includes a number of time patterns at corresponding frequencies. The method includes processing a number of frequency components of a frequency domain representation of the acoustic signal over time to determine a corresponding plurality of values. That processing includes determining, for each frequency component, a value characterizing a presence of a time pattern associated with at least one siren pattern. The method also includes processing the values according to the siren patterns to determine a detection result indicating whether the multi-tone siren type is present in the acoustic signal.