Predicting rider entry time for pick-up and drop-off locations
US-2021089788-A1 · Mar 25, 2021 · US
US11712995B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-11712995-B2 |
| Application number | US-202217984805-A |
| Country | US |
| Kind code | B2 |
| Filing date | Nov 10, 2022 |
| Priority date | Mar 12, 2019 |
| Publication date | Aug 1, 2023 |
| Grant date | Aug 1, 2023 |
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Aspects of the disclosure provide a method of facilitating communications from an autonomous vehicle to a user. For instance, a method may include, while attempting to pick up the user and prior to the user entering an vehicle, inputting a current location of the vehicle and map information into a model in order to identify a type of communication action for communicating a location of the vehicle to the user; enabling a first communication based on the type of the communication action; determining whether the user has responded to the first communication from received sensor data; and enabling a second communication based on the determination of whether the user has responded to the communication.
Opening claim text (preview).
The invention claimed is: 1. A computer-implemented method, comprising: determining, by one or more processors of a computing device, conditions in an environment of an autonomous vehicle; selecting, by the one or more processors using a trained model, a type of communication of a plurality of possible types of communications to be generated by the autonomous vehicle based on the determined conditions; and initiating, by the one or more processors, generation of the selected type of communication by the autonomous vehicle, wherein the selecting is responsive to a request received from a user of a client computing device for the autonomous vehicle to assist the user in reaching the autonomous vehicle. 2. The computer-implemented method of claim 1 , wherein the conditions are ambient lighting conditions. 3. The computer-implemented method of claim 2 , wherein the selected type of communication is generated by flashing headlights of the autonomous vehicle. 4. The computer-implemented method of claim 3 , wherein the ambient lighting conditions indicate whether the environment is dark enough to use the headlights. 5. The computer-implemented method of claim 2 , wherein the ambient lighting conditions are determined based on feedback from light sensors of the autonomous vehicle. 6. The computer-implemented method of claim 2 , wherein the ambient lighting conditions are determined based on at least one of a state of headlights of the autonomous vehicle or internal electronic displays in the autonomous vehicle. 7. The computer-implemented method of claim 2 , wherein the ambient lighting conditions are determined based on data generated by a perception system of the autonomous vehicle. 8. The computer-implemented method of claim 1 , wherein the selected type of communication is generated by honking a horn of the autonomous vehicle. 9. The computer-implemented method of claim 1 , wherein the selected type of communication is selected further based on time of day. 10. The computer-implemented method of claim 1 , wherein the selected type of communication is generated by displaying information on a display externally mounted on the autonomous vehicle. 11. The computer-implemented method of claim 1 , wherein the selected type of communication is generated by one or more speakers of the autonomous vehicle. 12. The computer-implemented method of claim 1 , further comprising: enabling, by the one or more processors, surfacing of one or more options on the client computing device of the user to enable the user to cause the autonomous vehicle to generate the selected type of communication. 13. The computer-implemented method of claim 1 , wherein the model is trained to identify patterns which increase a likelihood that the user will reach an autonomous vehicle more quickly in response to a vehicle communication. 14. The computer-implemented method of claim 13 , wherein the patterns indicate one or more escalated communications previously initiated by the user to minimize an amount of time it took the user to reach the autonomous vehicle. 15. A computer-implemented method, comprising: selecting, by one or more processors of a computing device using a trained model, a type of communication of a plurality of possible types of communications to be generated by an autonomous vehicle based on a current time of day; and initiating, by the one or more processors, generation of the selected type of communication by the autonomous vehicle, wherein the selecting is responsive to a request received from a user of a client computing device for the autonomous vehicle to assist the user in reaching the autonomous vehicle. 16. The computer-implemented method of claim 15 , wherein the selected type of communication is generated by flashing headlights of the autonomous vehicle. 17. The computer-implemented method of claim 15 , wherein the selected type of communication is generated by honking a horn of the autonomous vehicle. 18. A computing device comprising: one or more processors configured to: determine conditions in an environment of an autonomous vehicle; select, using a trained model, a type of communication of a plurality of possible types of communications to be generated by the autonomous vehicle based on the determined conditions; and initiate generation of the selected type of communication by the autonomous vehicle, wherein the selection of the type of communication is responsive to a request received from a user of a client computing device for the autonomous vehicle to assist the user in reaching the autonomous vehicle. 19. The computing device of claim 18 , wherein the conditions are ambient lighting conditions. 20. The computing device of claim 18 , wherein the selected type of communication is generated by flashing headlights of the autonomous vehicle.
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