US-20260126525-A1 - DETECTION AND CLASSIFICATION OF TUNNELS USING LIDAR

Abstract

A driver assistance system includes a light detection and ranging (LiDAR) sensor. A detecting module is configured to detect tunnels in a path of the vehicle. The detecting module includes a zoning module configured to bin the returns from the LiDAR sensor into a plurality of zones. A clustering and feature extraction module is configured to identify clusters in the zones, determine centers and variances of the clusters in x-axis, y-axis, and z-axis directions in the plurality of zones, identify a plurality of features based on the centers and the variances of the clusters, and concatenate the plurality of features into one or more concatenated features. A classification module is configured to receive the one or more concatenated features and to declare at least one of an approaching state, an inside state, and a clear state for a tunnel in response to the one or more concatenated features.

Inventors

• Siva Chinthalapudi
• Brent Navin Roger Bacchus
• Thanura Elvitigala

Assignees

• GM Global Technology Operations LLC

Dates

Publication Date

20260507

Application Date

20241101

Claims (20)

1. 1 . A driver assistance system for a vehicle, comprising: a light detection and ranging (LiDAR) sensor configured to transmit light pulses and to receive returns; and a detecting module configured to detect tunnels in a path of the vehicle including: a zoning module configured to bin the returns from the LiDAR sensor into a plurality of zones; a clustering and feature extraction module configured to: identify clusters in the zones, determine centers and variances of the clusters in x-axis, y-axis, and z-axis directions in the plurality of zones, identify a plurality of features based on the centers and the variances of the clusters, and concatenate the plurality of features into one or more concatenated features; and a classification module configured to receive the one or more concatenated features and to declare at least one of an approaching state, an inside state, and a clear state for a tunnel in response to the one or more concatenated features.
2. 2 . The driver assistance system of claim 1 , wherein the plurality of zones include a first zone and a second zone.
3. 3 . The driver assistance system of claim 2 , wherein the first zone corresponds to the returns from the LiDAR sensor with values in the z-axis direction that are less than a predetermined height and the second zone corresponds to the returns with values in the z-axis direction greater than the predetermined height.
4. 4 . The driver assistance system of claim 1 , further comprising a global position system (GPS), wherein the LiDAR sensor converts the returns into a Fernet frame in response to data from the GPS system.
5. 5 . The driver assistance system of claim 4 , further comprising an inertial measurement system configured to detect a pitch of the vehicle, wherein the LiDAR sensor compensates the returns in response to the pitch of the vehicle.
6. 6 . The driver assistance system of claim 3 , wherein the classifier module includes a pre-trained model configured to detect the approaching state, the inside state, and the clear state in response to the one or more concatenated features.
7. 7 . The driver assistance system of claim 6 , further comprising a filter module configured to filter an output of the classifier module using a Hidden Markov Model.
8. 8 . The driver assistance system of claim 7 , wherein the Hidden Markov Model filters out infeasible state transitions.
9. 9 . The driver assistance system of claim 6 , wherein the pre-trained model is configured to detect: the approaching state in response to the variances of the clusters in the x-axis direction in the second zone being less than a first variance and the variances of the clusters in the z-axis direction in the second zone being greater than a second variance; and the inside state in response to the variances of the clusters in the x-axis direction in the second zone being greater than a third variance and the variances of the clusters in the z-axis direction in the second zone being less than a fourth variance, wherein the first variance is less than the third variance and the third variance is greater than the fourth variance.
10. 10 . The driver assistance system of claim 1 , wherein the classifier module is configured to detect a wall in a path of the vehicle in response to the variances of the clusters in the x-axis direction and the z-axis direction.
11. 11 . A method for assisting a driver of for a vehicle, comprising: transmitting light pulses and to receive returns using a light detection and ranging (LiDAR) sensor; binning the returns from the LiDAR sensor into a plurality of zones; identifying clusters in the zones; determining centers and variances of the clusters in x-axis, y-axis, and z-axis directions in the plurality of zones; identifying a plurality of features based on the centers and the variances; concatenating the plurality of features into one or more concatenated features; and detecting at least one of an approaching state, an inside state, and a clear state for a tunnel in response to the one or more concatenated features.
12. 12 . The method of claim 11 , wherein the plurality of zones include a first zone and a second zone.
13. 13 . The method of claim 12 , wherein the first zone corresponds to the returns from the LiDAR sensor with values in the z-axis direction less than a predetermined height and the second zone corresponds to the returns with values in the z-axis direction greater than the predetermined height.
14. 14 . The method of claim 11 , further comprising converting the returns into a Fernet frame.
15. 15 . The method of claim 14 , further comprising: detecting pitch of the vehicle; and compensating the returns from the LiDAR sensor in response to the pitch of the vehicle.
16. 16 . The method of claim 13 , further comprising using a pre-trained model to detect the approaching state, the inside state, and the clear state in response to the one or more concatenated features.
17. 17 . The method of claim 16 , further comprising filtering an output of the pre-trained model using a Hidden Markov Model.
18. 18 . The method of claim 17 , wherein the Hidden Markov Model filters out infeasible state transitions.
19. 19 . The method of claim 16 , wherein the pre-trained model is configured to detect: the approaching state in response to the variances of the clusters in the x-axis direction in the second zone being less than a first variance and the variances of the clusters in the z-axis direction in the second zone being greater than a second variance; and the inside state in response to the variances of the clusters in the x-axis direction in the second zone being greater than a third variance and the variances of the clusters in the z-axis direction in the second zone being less than a fourth variance, wherein the first variance is less than the third variance and the third variance is greater than the fourth variance.
20. 20 . The method of claim 16 , wherein the pre-trained model is configured to detect a wall in a path of the vehicle in response to the variances in the x-axis direction and the z-axis direction.

Description

INTRODUCTION The information provided in this section is for the purpose of generally presenting the context of the disclosure. Work of the presently named inventors, to the extent it is described in this section, as well as aspects of the description that may not otherwise qualify as prior art at the time of filing, are neither expressly nor impliedly admitted as prior art against the present disclosure. The present disclosure relates to driver assistance systems, and more particularly to driver assistance systems including light detection and ranging (LiDAR) sensors. Vehicles including various levels of driver assistance (such as fully or partially autonomous vehicles) often rely on radio detection and ranging (radar) systems to detect and avoid objects in the path of the vehicle. Radar-based systems experience problems detecting objects when the vehicle is travelling through tunnels (or other similar infrastructure such as under overpasses). For example, radar-based systems detect ghost objects and/or errant tracks caused by tunnel infrastructure when moving through tunnels. SUMMARY A driver assistance system for a vehicle includes a light detection and ranging (LiDAR) sensor configured to transmit light pulses and to receive returns. A detecting module is configured to detect tunnels in a path of the vehicle. The detecting module includes a zoning module configured to bin the returns from the LiDAR sensor into a plurality of zones. The detecting module includes a clustering and feature extraction module configured to identify clusters in the zones, determine centers and variances of the clusters in x-axis, y-axis, and z-axis directions in the plurality of zones, identify a plurality of features based on the centers and the variances of the clusters, and concatenate the plurality of features into one or more concatenated features. A classification module is configured to receive the one or more concatenated features and to declare at least one of an approaching state, an inside state, and a clear state for a tunnel in response to the one or more concatenated features. In other features the plurality of zones include a first zone and a second zone. The first zone corresponds to the returns from the LiDAR sensor with values in the z-axis direction that are less than a predetermined height. The second zone corresponds to the returns with values in the z-axis direction greater than the predetermined height. The driver assistance system includes a global position system (GPS). The LiDAR sensor converts the returns into a Fernet frame in response to data from the GPS system. In other features, an inertial measurement system is configured to detect a pitch of the vehicle, wherein the LiDAR sensor compensates the returns in response to the pitch of the vehicle. The classifier module includes a pre-trained model configured to detect the approaching state, the inside state, and the clear state in response to the one or more concatenated features. A filter module is configured to filter an output of the classifier module using a Hidden Markov Model. The Hidden Markov Model filters out infeasible state transitions. In other features, the pre-trained model is configured to detect the approaching state in response to the variances of the clusters in the x-axis direction in the second zone being less than a first variance and the variances of the clusters in the z-axis direction in the second zone being greater than a second variance and the inside state in response to the variances of the clusters in the x-axis direction in the second zone being greater than a third variance and the variances of the clusters in the z-axis direction in the second zone being less than a fourth variance, wherein the first variance is less than the third variance and the third variance is greater than the fourth variance. In other features, the classifier module is configured to detect a wall in a path of the vehicle in response to the variances of the clusters in the x-axis direction and the z-axis direction. A method for assisting a driver of for a vehicle includes transmitting light pulses and to receive returns using a light detection and ranging (LiDAR) sensor; binning the returns from the LiDAR sensor into a plurality of zones; identifying clusters in the zones; determining centers and variances of the clusters in x-axis, y-axis, and z-axis directions in the plurality of zones; identifying a plurality of features based on the centers and the variances; concatenating the plurality of features into one or more concatenated features; and detecting at least one of an approaching state, an inside state, and a clear state for a tunnel in response to the one or more concatenated features. In other features, the plurality of zones include a first zone and a second zone. The first zone corresponds to the returns from the LiDAR sensor with values in the z-axis direction less than a predetermined height and the second zone corresponds to the r