System and method for providing vehicle safety distance and speed alerts under slippery road conditions

#The present disclosure relates generally to the automotive field. More particularly, the present disclosure relates to a system and method for providing vehicle safety distance and speed alerts under slippery road conditions.

#Various automotive manufacturers are currently developing and providing advanced driver assistance system (ADAS) and autonomous driving (AD) functionalities, such as safety distance and speed alerts, adaptive cruise control (ACC), and forward collision warning and response. These active safety features identify and track both static obstacles and active targets using different sensor modalities, such as radar, lidar, and cameras. The active safety features are functional and effective as long as road surface conditions are good. However, once the road surface conditions are compromised, by heavy rain, snow, or the like, the active safety features are rendered essentially ineffective, as they do not typically take into account extreme weather conditions and the road surface conditions.

#Thus, what is still needed in the art is an active safety system and method that is robust and incorporates the consideration of road surface condition into a safety distance and speed alert, ACC, and/or forward collision warning and response scheme, providing enhanced warning information to a driver and/or active control to a vehicle.

#The present disclosure is illustrated and described with reference to the various drawings, in which like reference numbers are used to denote like system components/method steps, as appropriate, and in which:

#FIG. 1 is a schematic diagram illustrating the various distances and road frictions considered by the system and method of the present disclosure;

#FIG. 2 is a schematic diagram illustrating one exemplary embodiment of the system and method of the present disclosure implemented in a closed-loop, ACC context;

#Again, various driver warning information and ACC and PA functions currently available promote the driver or vehicle maintenance of a safe distance and speed between a following vehicle and a preceding vehicle, for example, by, at the following vehicle, calculating the speed of the preceding vehicle, the speed of the following vehicle, and the distance between the following vehicle and the preceding vehicle. Again, such calculations are performed using one or more onboard sensors and/or cameras, as well as an onboard and/or cloud-based processing system. The present disclosure supplements these calculations by utilizing information, such as a detected road friction at the following vehicle and a predicted road friction by the following vehicle. The predicted road friction may be based on local observations and/or cloud-based observations obtained from other vehicles. The detected road friction between the following vehicle tires and the road surface may be assessed using a variety of methodologies and is used to compute a critical safety distance between the following vehicle and the preceding vehicle and a critical safety speed of the following vehicle. The predicted road friction ahead of the following vehicle may also be assessed using a variety of methodologies (lidar, camera, and cloud-based examples are provided) and is used to compute a warning safety distance between the following vehicle and the preceding vehicle and a warning safety speed of the following vehicle. These functionalities may of course be applied to vehicle/stationary object warning and response scenarios and the like as well.

#Thus, while most conventional systems and methods rely on longitudinal velocity control mechanisms for the initiation of vehicle throttle and braking controls in detected scenarios, the present disclosure utilizes ML models to detect the road friction or road surface condition beneath a vehicle and predict the road friction or road surface condition ahead of a vehicle—thereby improving vehicle warning and response systems related to other vehicles, obstacles, and the like. The result is increased accuracy of and confidence in determined critical and warning safety distances and speeds.

#Referring now specifically to FIG. 1, in one exemplary embodiment, the various distances and road frictions considered by the system and method of the present disclosure include: μd (detected friction)—the detected friction coincident with the ego vehicle 10 based on ML modeling using onboard sensors; μp (predicted friction)—the predicted friction ahead of the ego vehicle 10 based on ML modeling using vision/perception sensors; d (distance)—the distance between the ego vehicle 10 and a preceding vehicle 12 or other object (moving or stationary); Ds (critical safety distance)—the critical required stopping distance for the ego vehicle 10 in view of the detected friction and predicted friction as calculated by the collision avoidance or ACC module, Ds=v2/(2*g*f(μd, μp)), where g is the gravitational constant; Dw (warning safety distance)—the warning required stopping distance for the ego vehicle 10 in view of the predicted friction as calculated by the collision avoidance or ACC module, Dw=v2/(2*a*f(μp)), where a is maximum acceleration/deceleration of the ego vehicle; v (velocity)—the velocity of the ego vehicle 10; and Vds (desired velocity)—the recommended velocity to maintain the critical safety distance between the ego vehicle 10 and a preceding vehicle 12 or other object (moving or stationary).