System and method for managing environmental conditions for an autonomous vehicle

Abstract

Systems and methods for managing environmental conditions for an autonomous vehicle are disclosed. In one aspect, an autonomous vehicle includes a perception sensor configured to generate perception data indicative of a condition of the environment, a network communication transceiver configured to communicate with an oversight system and an external weather condition source, a non-transitory computer readable medium, and a processor. The processor is configured to: receive the perception data from the at least one perception sensor, receive an indication of current weather conditions from the external weather condition source, determine a current environmental condition severity level from a plurality of severity levels based on the perception data and the indication of current weather conditions, modify one or more driving parameters that that govern a range of actions that can be autonomously executed by the autonomous vehicle, and navigate the autonomous vehicle based on the modified driving parameters.

What is claimed is: 
     
         1 . An autonomous vehicle comprising:
 at least one perception sensor configured to generate perception data indicative of a condition of the environment;   a network communication transceiver configured to communicate with an oversight system;   a non-transitory computer readable medium; and   a processor configured to: 
 receive the perception data from the at least one perception sensor, 
 receive an indication of the condition of the environment from the oversight system, 
 determine an expected condition of the environment at a future time based on the perception data and the indication of the condition of the environment, 
 predict a road traction level for the autonomous vehicle at the future time based on the expected condition of the environment, and 
 navigate the autonomous vehicle based on the predicted road traction level. 
   
     
     
         2 . The autonomous vehicle of  claim 1 , wherein the processor is further configured to:
 receive an expected temperature of the environment at the future time from the oversight system,   wherein the predicting of the road traction level for the autonomous vehicle at the future time is further based on the expected temperature of the environment.   
     
     
         3 . The autonomous vehicle of  claim 2 , wherein the predicting of the road traction level for the autonomous vehicle at the future time comprises:
 determining that there is a risk of low road traction at the future time in response to the expected temperature being less than a threshold temperature.   
     
     
         4 . The autonomous vehicle of  claim 1 , wherein the processor is further configured to:
 determine a road surface type based on a predetermined map stored in the non-transitory computer readable medium,   wherein the predicting of the road traction level for the autonomous vehicle at the future time is further based on the determined road surface type.   
     
     
         5 . The autonomous vehicle of  claim 4 , wherein the determining of the road surface type is further based on the perception data. 
     
     
         6 . The autonomous vehicle of  claim 1 , wherein the processor is further configured to:
 detect understeering of the autonomous vehicle based on the perception data, and   estimate a current road traction level for the autonomous vehicle based on the detected understeering of the autonomous vehicle.   
     
     
         7 . The autonomous vehicle of  claim 1 , wherein the processor is further configured to:
 detect a response of the autonomous vehicle to a command to accelerate the autonomous vehicle, and   estimate a current road traction level for the autonomous vehicle based on the detected response of the autonomous vehicle to the command to accelerate the autonomous vehicle.   
     
     
         8 . The autonomous vehicle of  claim 1 , wherein the processor is further configured to:
 detect a response of the autonomous vehicle to a command to apply a torque to one or more wheels of the autonomous vehicle, and   estimate a current road traction level for the autonomous vehicle based on the detected response of the autonomous vehicle to a command to apply the torque to the one or more wheels of the autonomous vehicle.   
     
     
         9 . The autonomous vehicle of  claim 8 , wherein:
 the detecting the response of the autonomous vehicle to the command to apply the torque to the one or more wheels of the autonomous vehicle comprises determining a slip rate from tires of the autonomous vehicle in response to the command to apply the torque to the one or more wheels, and   the estimating the current road traction level for the autonomous vehicle is further based on the determined slip rate from the tires of the autonomous vehicle.   
     
     
         10 . The autonomous vehicle of  claim 1 , wherein the processor is further configured to:
 determine that the predicted road traction level is less than a predetermined road traction level, and   in response to determining that the predicted road traction level is less than the predetermined road traction level, limit lateral acceleration of the autonomous vehicle to a threshold lateral acceleration value.   
     
     
         11 . The autonomous vehicle of  claim 10 , wherein the processor is further configured to:
 in response to determining that the predicted road traction level is less than the predetermined road traction level, limit jerk of the autonomous vehicle to a threshold jerk value.   
     
     
         12 . A non-transitory computer-readable medium having stored thereon instructions which, when executed by a processor, cause the processor to:
 receive perception data from at least one perception sensor of an autonomous vehicle, the at least one perception sensor configured to generate perception data indicative of a condition of the environment;   receive an indication of the condition of the environment from an oversight system via a network communication transceiver of the autonomous vehicle;   determine an expected condition of the environment at a future time based on the perception data and the indication of the condition of the environment;   predict a road traction level for the autonomous vehicle at the future time based on the expected condition of the environment; and   navigate the autonomous vehicle based on the predicted road traction level.   
     
     
         13 . The non-transitory computer-readable medium of  claim 12 , wherein the instructions further cause the processor to:
 dynamically adjust a following distance from other vehicles directly ahead of the autonomous vehicle based at least in part on the predicted road traction level and a safe longitudinal deceleration required to completely stop the autonomous vehicle.   
     
     
         14 . The non-transitory computer-readable medium of  claim 13 , wherein the instructions further cause the processor to:
 determine the safe longitudinal deceleration required to completely stop the autonomous vehicle based on a road surface type and the predicted road traction level.   
     
     
         15 . The non-transitory computer-readable medium of  claim 13 , wherein the dynamically adjusting the following distance instructions comprise:
 determining the following distance to be greater than the safe longitudinal deceleration required to completely stop the autonomous vehicle.   
     
     
         16 . The non-transitory computer-readable medium of  claim 12 , wherein the instructions further cause the processor to:
 determine that the predicted road traction level is less than a threshold road traction level, and   in response to determining that the predicted road traction level is less than the threshold road traction level, cause the autonomous vehicle to execute a minimal risk condition (MRC) maneuver.   
     
     
         17 . The non-transitory computer-readable medium of  claim 16 , wherein the MRC maneuver comprises pulling the autonomous vehicle over to a safe zone of a roadway. 
     
     
         18 . A method, comprising:
 receiving perception data from at least one perception sensor of an autonomous vehicle, the at least one perception sensor configured to generate perception data indicative of a condition of the environment;   receiving an indication of the condition of the environment from an oversight system via a network communication transceiver of the autonomous vehicle;   determining an expected condition of the environment at a future time based on the perception data and the indication of the condition of the environment;   predicting a road traction level for the autonomous vehicle at the future time based on the expected condition of the environment; and   navigating the autonomous vehicle based on the predicted road traction level.   
     
     
         19 . The method of  claim 18 , further comprising:
 determining that the autonomous vehicle will operate out of an operational design domain (ODD) of the autonomous vehicle at the future time based on the predicted road traction level, and   in response to determining that the autonomous vehicle will operating out of the ODD, causing the autonomous vehicle to execute a first minimum risk condition (MRC) maneuver.   
     
     
         20 . The method of  claim 19 , further comprising:
 determining that the autonomous vehicle has been attempting the first MRC for longer than a predetermined period of time without success, and   in response to determining that the autonomous vehicle has been attempting the first MRC for longer than a predetermined period of time without success, causing the autonomous vehicle to execute a second MRC maneuver.