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.

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.

A vehicle sideslip estimation system includes sensors mounted on a vehicle and a kinematic model receiving signals from the sensors to estimate a lateral velocity of the vehicle. A compensated acceleration calculator calculates a compensated lateral acceleration as a measure of conditions that result in a deviation of a measured lateral acceleration. A lateral acceleration calculator determines, based on the compensated lateral acceleration and the measured lateral acceleration, if a lateral acceleration error is larger than a predefined threshold. A filter corrects the estimated lateral velocity of the vehicle when the lateral acceleration error is larger than the predefined threshold. A velocity output register registers the estimated lateral velocity of the vehicle when the lateral acceleration error is smaller than the predefined threshold, and a sideslip calculator calculates a sideslip angle of the vehicle in real time from the registered lateral velocity of the vehicle and a vehicle longitudinal velocity.

A vehicle sideslip estimation system includes sensors mounted on a vehicle and a kinematic model receiving signals from the sensors to estimate a lateral velocity of the vehicle. A compensated acceleration calculator calculates a compensated lateral acceleration as a measure of conditions that result in a deviation of a measured lateral acceleration. A lateral acceleration calculator determines, based on the compensated lateral acceleration and the measured lateral acceleration, if a lateral acceleration error is larger than a predefined threshold. A filter corrects the estimated lateral velocity of the vehicle when the lateral acceleration error is larger than the predefined threshold. A velocity output register registers the estimated lateral velocity of the vehicle when the lateral acceleration error is smaller than the predefined threshold, and a sideslip calculator calculates a sideslip angle of the vehicle in real time from the registered lateral velocity of the vehicle and a vehicle longitudinal velocity.

Vehicle alert and control systems and methods taking into account a detected road friction at a following vehicle and a predicted road friction by the following vehicle. 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 be applied to vehicle/stationary object warning and response scenarios as well.

Vehicle alert and control systems and methods taking into account a detected road friction at a following vehicle and a predicted road friction by the following vehicle. 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 be applied to vehicle/stationary object warning and response scenarios as well.

A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: predict, at a trained machine learning classifier, a target slip value based on a predicted slip slope and a predicted road texture, wherein the predicted slip slope and the predicted road texture are determined using sensor data representing tire forces and modify at least one vehicle action based on the target slip value when a confidence level value corresponding to the target slip value is greater than or equal to a confidence level threshold.

A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: predict, at a trained machine learning classifier, a target slip value based on a predicted slip slope and a predicted road texture, wherein the predicted slip slope and the predicted road texture are determined using sensor data representing tire forces and modify at least one vehicle action based on the target slip value when a confidence level value corresponding to the target slip value is greater than or equal to a confidence level threshold.

Techniques are described for estimating road friction between a road and tires of a vehicle. A method includes receiving, from a temperature sensor on a vehicle, a temperature value that indicates a temperature of an environment in which a vehicle is operated, determining a first range of friction values that quantify a friction between a road and tires of a vehicle based on a function of the temperature value and an extent of precipitation in a region that indicate a hazardous driving condition, obtaining, from the first range of friction values, a value that quantifies the friction between the road and the tires of the vehicle, where the value is obtained based on a driving related behavior of the vehicle, and causing the vehicle to operate on the road based on the value obtained from the first range of friction values.