PROCESSING A REQUEST SIGNAL REGARDING OPERATION OF AN AUTONOMOUS VEHICLE
Among other things, a vehicle drives autonomously on a trajectory through a road network to a goal location based on an automatic process for planning the trajectory without human intervention; and an automatic process alters the planning of the trajectory to reach a target location based on a request received from an occupant of the vehicle to engage in a speed-reducing maneuver.
1 . A method comprising:
(a) causing a vehicle to drive autonomously on a road network,
(b) by a computer receiving a signal representing a request for an emergency stop or an identification of an emergency condition,
(b) by a computer analyzing the request or the emergency condition and data, to identify a target location for stopping, and
(c) causing the vehicle to move autonomously to and stop at the target location.
2 . The method of claim 1 , comprising receiving the signal from (a) an occupant, (b) a teleoperator, or (c) a software or hardware process that identifies the emergency condition.
3 . The method of claim 1 , in which receiving the signal comprises receiving at least some of the data from a user interface in the vehicle resulting from an interaction by an occupant of the vehicle.
4 . The method of claim 3 , in which the interface comprises a button, a switch, a pull-cord, a pedal, a pad, a sensor, a whistle, a display, or a voice assistant, or a combination of two or more of them.
5 . The method of claim 1 , in which receiving the signal comprises receiving a command from a teleoperator via telecommunications.
6 . The method of claim 1 , in which the signal is initiated by a software or hardware process based on degradation in performance of the vehicle.
7 . The method of claim 6 , in which the degradation in performance comprises degradation in performance of a sensor or a component of the vehicle.
8 . The method of claim 1 , in which the signal is initiated by detection of an event on the road network.
9 . The method of claim 1 , in which the data or the signal comprises a degree of urgency.
10 . The method of claim 9 , in which the degree of urgency has been indicated an initiator of the signal.
11 . The method of claim 9 , comprising inferring the degree of urgency by an algorithmic analysis on the signal.
12 . The method of claim 11 , in which the algorithmic analysis comprises analysis of a frequency, a volume, a sound, a voice, or a type, or two or more of them, of an oral request.
13 . The method of claim 1 , in which the data or the signal comprises an expected stopping time interval.
14 . The method of claim 13 , in which the expected stopping time interval is received from an initiator of the signal.
15 . The method of claim 13 , in which the expected stopping time interval is inferred by an algorithmic analysis on the signal.
16 . The method of claim 15 , in which the algorithmic analysis comprises analyzing the data.
17 . The method of claim 1 , in which the data comprises traffic data, sensor data, map data, or two or more of them.
18 . The method of claim 1 , in which analyzing the request comprises evaluating quality of one or more target locations.
19 . The method of claim 18 , in which the quality is evaluated offline relative to the vehicle.
20 . The method of claim 18 , in which the quality is evaluated online when the vehicle is driving.
21 . The method of claim 20 , in which evaluating the quality comprises computing a quality based on one or more of the following factors: the emergency condition, a location of the vehicle on the road network, a traffic speed, a traffic volume, a traffic composition, a lane choice, a blockage degree, a sightline from another vehicle, a distance from an intersection, presence of a dedicated lane, terrain, and a road slope.
22 . The method of claim 21 , in which one or more of the factors are represented by numerical values.
23 . The method of claim 22 , in which each of the numerical values is mapped to a pre-defined range.
24 . The method of claim 21 , in which the computing of the quality comprises assigning weights to one or more of the factors.
25 . The method of claim 24 , in which the weights are based on one or more of the following: features of the request, a type of the vehicle, a regulation, a degree of urgency, and an expected stopping time interval.
26 . The method of claim 21 , comprising ignoring a target location based on its quality value or information specifying its exclusion.
27 . The method of claim 21 , comprising classifying target locations in quality categories.
28 . The method of claim 1 , in which the analyzing comprises applying a minimum quality threshold to identify one or more acceptable target locations.
29 . The method of claim 28 , comprising computing the minimum quality threshold based on one or more of the following: a degree of urgency, an expected stopping time interval, and a conflicting rule.
30 . The method of claim 1 , in which analyzing the data comprises identifying a region for choosing a target location.
31 . The method of claim 30 , in which the region comprises a drivable area or a non-drivable area or both.
32 . The method of claim 30 , in which the region comprises an area faced by a forward-facing side of the vehicle.
33 . The method of claim 30 , in which the region comprises a shape or a size or both.
34 . The method of claim 33 , comprising determining the shape or the size based on a traffic condition or on a degree of urgency or on both.
35 . The method of claim 30 , comprising determining the region based on qualities of one or more target locations.
36 . The method of claim 30 , comprising expanding the region when no target location is identified.
37 . The method of claim 30 , comprising updating the region based on a new location of the vehicle.
38 . The method of claim 30 , in which choosing a target location comprises discretizing the region into potential target places.
39 . The method of claim 30 , in which choosing a target location comprises using availability data.
40 . The method of claim 39 , comprising acquiring the availability data from one or more of the following: a crowd-sourced database, a sensor, a perception process, a historical database, a parking lot database, a parking space database, the vehicle, and another vehicle.