# US 2024/0199088 A1 · Systems and methods for adapting autonomous driving escalation strategies > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2024/0199088 A1 - Document ID: US20240199088A1 - Status: Published application - Kind: A1 - Application number: 18/066,994 - Filed: 2022-12-15 - Published: 2024-06-20 - Earliest claimed: 2022-12-15 - Assignees: GM Global Technology Operations LLC - Applicants: GM Global Technology Operations LLC - Inventors: Parisa Mahvelatishamsabadi; Milad Jalaliyazdi; Zhi Li; Mohammadali Shahriari; Peitong Hu; Kin Man Michael Wong - Web page: https://patentgrep.com/patent/US20240199088A1 ## CPC - B60W 60/0053: {from vehicle to occupant} - B60W 50/14: Means for informing the driver, warning the driver or prompting a driver intervention - B60W 2540/229: Attention level, e.g. attentive to driving, reading or sleeping - B60W 2552/05: Type of road, e.g. motorways, local streets, paved or unpaved roads - B60W 2552/30: Road curve radius - B60W 2554/40: Dynamic objects, e.g. animals, windblown objects - B60W 2555/20: Ambient conditions, e.g. wind or rain - B60W 2556/10: Historical data - B60W: CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT ## Abstract Methods and systems are provided for alerting a driver to take control of a vehicle. In one embodiment, a method includes: determining, by a processor, a driver alertness level based on a weighted summation of a first set of feature data; determining, by the processor, a required alertness level based on a weighted summation of a second set of feature data; determining, by the processor, an escalation index based on the driver alertness level and the required alertness level; and generating, by the processor, alert notification data based on the escalation index. ## Claims ### Claim 1 A method of alerting a driver to take control of a vehicle, comprising: determining, by a processor, a driver alertness level based on a weighted summation of a first set of feature data; determining, by the processor, a required alertness level based on a weighted summation of a second set of feature data; determining, by the processor, an escalation index based on the driver alertness level and the required alertness level; and generating, by the processor, alert notification data based on the escalation index. ### Claim 2 (depends on 1) The method of claim 1, wherein the determining the escalation index comprises comparing the driver alertness level with the required alertness level, and when the driver alertness level falls below the required alertness level, determining the escalation index based on a deficiency in the driver alertness level. ### Claim 3 (depends on 1) The method of claim 1, wherein the escalation index includes an escalation pace of notifying the driver via the alert notification data. ### Claim 4 (depends on 1) The method of claim 1, further comprising determining a weight associated with each feature of the feature data, and wherein the determining the driver alertness level is based on the weights. ### Claim 5 (depends on 4) The method of claim 4, wherein the determining the weight is based on a trained classification model stored in a data storage device of the vehicle. ### Claim 6 (depends on 5) The method of claim 5, further comprising training the classification model based on a normalization of feature data associated with various vehicle events deemed to be risky with respect to a baseline distribution to establish a relative importance of features of the vehicle events. ### Claim 7 (depends on 4) The method of claim 4, wherein the determining the weight is based on predetermined weights stored in a data storage device of the vehicle. ### Claim 8 (depends on 1) The method of claim 1, wherein the first set of feature data includes at least one of a surrounding traffic, a number of intersections, a road lane quality, a road lane curvature, a weather condition, and wind speed. ### Claim 9 (depends on 1) The method of claim 1, wherein the first set of feature data includes at least one of a steering tracking error, a target lane tracking error, a steering busyness, a lane touch count, and an inertia measurement unit bias. ### Claim 10 (depends on 1) The method of claim 1, wherein the second set of feature data includes at least one of a driver's hand position, a driver attention level, a driver reaction delay, and an escalation history. ### Claim 11 A system for alerting a driver of a vehicle, comprising: a non-transitory computer readable media encoded with programming instructions configured to, by a processor, determine a driver alertness level based on a weighted summation of a first set of feature data; determine a required alertness level based on a weighted summation of a second set of feature data; determine an escalation index based on the driver alertness level and the required alertness level; and generate alert notification data based on the escalation index. ### Claim 12 (depends on 11) The system of claim 11, wherein the programming instructions are configured to determine the escalation index by comparing the driver alertness level with the required alertness level, and when the driver alertness level falls below the required alertness level, determine the escalation index based on a deficiency in the driver alertness level. ### Claim 13 (depends on 11) The system of claim 11, wherein the escalation index includes an escalation pace of notifying the driver via the alert notification data. ### Claim 14 (depends on 11) The system of claim 11, wherein the programming instructions are further configured to determine a weight associated with each feature of the feature data, and determine the driver alertness level based on the weights. ### Claim 15 (depends on 14) The system of claim 14, wherein the programming instructions determine the weight based on a trained classification model stored in a data storage device of the vehicle. ### Claim 16 (depends on 15) The system of claim 15, wherein the programming instructions are further configured to train the classification model based on a normalization of feature data associated with various vehicle events deemed to be risky with respect to a baseline distribution to establish a relative importance of features of the vehicle events. ### Claim 17 (depends on 14) The system of claim 14, wherein the programming instructions are configured to determine the weight based on predetermined weights stored in a data storage device of the vehicle. ### Claim 18 (depends on 11) The system of claim 11, wherein the first set of feature data includes at least one of a surrounding traffic, a number of intersections, a road lane quality, a road lane curvature, a weather condition, and wind speed. ### Claim 19 (depends on 11) The system of claim 11, wherein the first set of feature data includes at least one of a steering tracking error, a target lane tracking error, a steering busyness, a lane touch count, and an inertia measurement unit bias. ### Claim 20 (depends on 11) The system of claim 11, wherein the second set of feature data includes at least one of a driver's hand position, a driver attention level, a driver reaction delay, and an escalation history.