# US 2026/0084718 A1 · Detecting and Responding to Malfunctioning Traffic Lights For Autonomous Vehicles

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- Number: US 2026/0084718 A1
- Document ID: US20260084718A1
- Status: Published application
- Kind: A1
- Application number: 19/405,553
- Filed: 2025-12-02
- Published: 2026-03-26
- Earliest claimed: 2020-06-19
- Assignees: Waymo LLC
- Applicants: Waymo LLC.
- Inventors: David Silver; Carl Kershaw; Jonathan Hsiao; Edward Hsiao
- Web page: https://patentgrep.com/patent/US20260084718A1

## CPC

- B60W 60/0015: {specially adapted for safety}
- B60W 60/0025: {specially adapted for specific operations}
- G06F 18/24: Classification techniques
- G06V 20/584: {of vehicle lights or traffic lights}
- G06V 40/10: Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G08G 1/095: Traffic lights
- G08G 1/09623: {Systems involving the acquisition of information from passive traffic signs by means mounted on the vehicle (G08G1/0967 takes precedence)}
- B60W 2554/4029: Pedestrians
- B60W 2554/408: Traffic behavior, e.g. swarm
- B60W 2555/60: Traffic rules, e.g. speed limits or right of way
- B60W 2556/10: Historical data
- B60W 2556/20: Data confidence level
- G08G 1/096725: {where the received information generates an automatic action on the vehicle control}
- 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
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G08G: TRAFFIC CONTROL SYSTEMS (guiding railway traffic, ensuring the safety of railway traffic B61L; arrangement of road signs or traffic signals E01F9/00; radar or analogous systems, sonar systems, lidar systems specially adapted for traffic control G01S13/91, G01S15/88, G01S17/88; {radar or analogous systems, sonar systems, lidar systems specially adapted for anti-collision purposes G01S13/93, G01S15/93, G01S17/93})

## Abstract

Aspects of the disclosure relate to detecting and responding to malfunctioning traffic signals for a vehicle having an autonomous driving mode. For instance, information identifying a detected state of a traffic signal for an intersection. An anomaly for the traffic signal may be detected based on the detected state and prestored information about expected states of the traffic signal. The vehicle may be controlled in the autonomous driving mode based on the detected anomaly.

## Claims

### Claim 1

A method comprising:
determining, by one or more processors of a vehicle, when the vehicle is within a threshold distance of a traffic signal for an intersection where a state of the traffic signal should be detected by the vehicle;
when the vehicle is within the threshold distance of the traffic signal and the state of the traffic signal is not detected, determine, by the one or more processors, that an anomaly exists for the traffic signal;
identifying, by the one or more processors, a classification of the anomaly from one of a plurality of different classifications of anomaly types; and
controlling, by the one or more processors, the vehicle in an autonomous driving mode based on the classification of the anomaly.

### Claim 2 (depends on 1)

The method of claim 1, wherein the state of the traffic signal is not detected because the traffic signal is malfunctioning.

### Claim 3 (depends on 1)

The method of claim 1, wherein detecting the anomaly includes determining whether there are occlusions between the vehicle and the traffic signal.

### Claim 4 (depends on 3)

The method of claim 3, wherein determining that an anomaly exists includes determining that there are no occlusions between the vehicle and the traffic signal.

### Claim 5 (depends on 1)

The method of claim 1, wherein the threshold distance is closer in distance or time to the traffic signal than a perceptive range of a system of the vehicle configured to detect states of traffic signals.

### Claim 6 (depends on 1)

The method of claim 1, wherein the threshold distance accounts for time for a system of the vehicle configured to detect states of traffic signals to detect the state of the traffic signal.

### Claim 7 (depends on 1)

The method of claim 1, wherein detecting the anomaly is further based on whether the vehicle has observed a same state of the traffic signal for more than a threshold period of time.

### Claim 8 (depends on 1)

The method of claim 1, wherein identifying the classification is based on a look up table which cross-references anomalies and ones of plurality of different types of anomalies.

### Claim 9 (depends on 8)

The method of claim 8, wherein the look up table further cross-references anomalies and the plurality of different classifications of anomaly types with responses, and wherein controlling the vehicle is further based on one of the responses.

### Claim 10

A system comprising one or more processors configured to:
determine when a vehicle is within a threshold distance of a traffic signal for an intersection where a state of the traffic signal should be detected by the vehicle;
when the vehicle is within the threshold distance of the traffic signal and the state of the traffic signal is not detected, determine that an anomaly exists for the traffic signal;
identify a classification of the anomaly from one of a plurality of different classifications of anomaly types; and
control the vehicle in an autonomous driving mode based on the classification of the anomaly.

### Claim 11 (depends on 10)

The system of claim 10, wherein the state of the traffic signal is not detected because the traffic signal is malfunctioning.

### Claim 12 (depends on 10)

The system of claim 10, wherein the one or more processors are further configured to detect the anomaly by determining whether there are occlusions between the vehicle and the traffic signal.

### Claim 13 (depends on 12)

The system of claim 12, wherein the one or more processors are further configured to determine that an anomaly exists by determining that there are no occlusions between the vehicle and the traffic signal.

### Claim 14 (depends on 10)

The system of claim 10, wherein the threshold distance is closer in distance or time to the traffic signal than a perceptive range of a system of the vehicle configured to detect states of traffic signals.

### Claim 15 (depends on 10)

The system of claim 10, wherein the threshold distance accounts for time for a system of the vehicle configured to detect states of traffic signals to detect the state of the traffic signal.

### Claim 16 (depends on 10)

The system of claim 10, wherein the one or more processors are further configured to detect the anomaly further based on whether the vehicle has observed a same state of the traffic signal for more than a threshold period of time.

### Claim 17 (depends on 10)

The system of claim 10, wherein the one or more processors are further configured to identify the classification further based on a look up table which cross-references anomalies and ones of plurality of different types of anomalies.

### Claim 18 (depends on 17)

The system of claim 17, wherein the look up table further cross-references anomalies and the plurality of different classifications of anomaly types with responses, and wherein the one or more processors are further configured to control the vehicle is further based on one of the responses.

### Claim 19 (depends on 10)

The system of claim 10, further comprising the vehicle.

### Claim 20

A non-transitory, computer readable medium on which instructions are stored, the instructions, when executed by one or more processors, cause the one or more processors to perform a method, the method comprising:
determining when a vehicle is within a threshold distance of a traffic signal for an intersection where a state of the traffic signal should be detected by the vehicle;
when the vehicle is within the threshold distance of the traffic signal and the state of the traffic signal is not detected, determining that an anomaly exists for the traffic signal;
identifying a classification of the anomaly from one of a plurality of different classifications of anomaly types; and
controlling the vehicle in an autonomous driving mode based on the classification of the anomaly.