Example embodiments relate to techniques for using vehicle image radar to estimate rain rate and other weather conditions. A computing device may receive radar data from a radar unit coupled to a vehicle. The radar data can represent the vehicle's environment. The computing device may use the radar data to determine a radar representation that indicates backscatter power and estimate, using a rain rate model, a rain rate for the environment based on the radar representation. The computing device may then control the vehicle based on the rain rate. In some examples, the computing device may provide the rain rate estimation and an indication of its current location to other vehicles to enable the vehicles to adjust routes based on the rain rate estimation.

Aspects of the disclosure relate to controlling a vehicle having an autonomous driving mode. This may include receiving, by one or more processors of the vehicle, first information identifying a current relative humidity measurement within a sensor housing of a vehicle having an autonomous driving mode. The relative humidity measurement and pre-stored environmental map information may be used by the one or more processors to estimate a condition of a sensor within the sensor housing at a future time. This estimated condition may be used by the one or more processors to control the vehicle.

A method includes detecting, via a processor of an autonomous vehicle, an upcoming downhill road segment of a route on which the autonomous vehicle is currently travelling. The detection is based on map data, camera data, and/or inertial measurement unit (IMU) data. In response to detecting the upcoming downhill road segment, a descent plan is generated for the autonomous vehicle. The descent plan includes a speed profile and a brake usage plan. The brake usage plan specifies a non-zero amount of retarder usage and an amount of foundation brake usage for a predefined time period. The method also includes autonomously controlling the autonomous vehicle, based on the descent plan, while the autonomous vehicle descends the downhill road segment.

This invention involves the effect of multiple rules of the road at different elevation profiles on the speed constraints and therefore the overall fuel efficiency. A vehicle designed to optimize fuel consumption that is comprised of the rules of the road that determine maximum speed, minimum speed, stop signs, streetlights, and/or changes in other rules that determine the allowable speeds of the road, a localization mechanism, and an optimization engine to optimize the fuel economy by selecting a speed profile within that maintains the vehicle within the assigned range of speeds and minimizes fuel consumption. A wide variety of methods that typically are used to optimize the fuel efficiency of human drivers operating standard vehicles can also be applied towards autonomous vehicles driving at different speed constraints and with different changes in the elevation.

A system and method of controlling components of a vehicle are disclosed. The method includes the steps of sensing a current position of a rear closure panel with a closure panel position sensor, sensing a speed of the vehicle with a speed sensor, and sensing a current environmental condition with an environmental precipitation sensor. The method also includes adjusting a degree of openness of at least one window as a result of the closure panel position sensor indicating that the rear closure panel is in an open position, the speed sensor indicating that a speed of the vehicle is greater than zero kilometers per hour (kph), and the current environmental condition that is indicated by the environmental precipitation sensor.

Example implementations may relate to sun-aware vehicle routing. In particular, a computing system of a vehicle may determine an expected position of the sun relative to a geographic area. Based on the expected position, the computing system may make a determination that travel of the vehicle through certain location(s) within the geographic area is expected to result in the sun being proximate to an object within a field of view of the vehicle's image capture device. Responsively, the computing system may generate a route for the vehicle in the geographic area based at least on the route avoiding travel of the vehicle through these certain location(s), and may then operate the vehicle to travel in accordance with the generated route. Ultimately, this may help reduce or prevent situations where quality of image(s) degrades due to sunlight, which may allow for use of these image(s) as basis for operating the vehicle.

Disclosed are a device and a method of controlling a remote parking assist function capable of determining in advance whether to enter, adjustment, or cancel the remote parking assist function using direct or indirect environment information. The device for controlling a remote parking assist function may collect direct and indirect environment information on a location where a vehicle is to be parked from a surrounding-environment information source, analyze the collected information, and cause activation of at least one of an entry control function, an adjustment control function, or a cancellation control function.

A method for quantifying vehicle path following performance, the method comprising; obtaining samples of path following performance (I), selecting a subset of the path following performance samples such that the selected samples follow a pre-determined statistical extreme value distribution, parameterizing the pre-determined statistical extreme value distribution based on the selected samples of path following performance, and quantifying vehicle path following performance based on the parameterized statistical extreme value distribution.

Systems and methods for situational behavior of an autonomous vehicle are disclosed. In one aspect, an autonomous vehicle includes at least one perception sensor configured to generate perception data indicative of at least one other vehicle on a roadway, a non-transitory computer readable medium, and a processor. The processor is configured to determine that the other vehicle is violating one or more rules of the roadway based on the perception data, tag the other vehicle as a non-compliant driver, and modify control of the autonomous vehicle in response to tagging the other vehicle as a non-compliant driver.

An automated method of controlling a speed of a vehicle includes identifying parameters of a driving instance of the vehicle; identifying a predetermined profile that is applicable to the driving instance based on the identified parameters; identifying a predetermined speed policy applicable to the driving instance based on the identified profile; and implementing the identified speed policy during the driving instance. The method may be repeated during the driving instance, whereby the speed policy that is implemented is automatically updated when one or more changes in the identified parameters cause a different predetermined speed policy to be identified. Parameter may include driver parameters (e.g., driver age and driver experience); vehicle parameters (e.g., vehicle age, mileage, and tire wear) tire maintenance information); behavior parameters (e.g., speed, acceleration, hard braking of the vehicle, following distance, swerving, and cornering); and circumstance parameters (e.g., time of day, road information, inclement weather, and traffic congestion).