CN-121995357-A - Detection and classification of tunnels using light detection and ranging

Abstract

A driver assistance system includes a light detection and ranging (LiDAR) sensor. The detection module is configured to detect a tunnel in a path of the vehicle. The detection module includes a zoning module configured to bin echoes from LiDAR sensors into a plurality of zones. The cluster and feature extraction module is configured to identify clusters in the region, determine centers and variances of the clusters in the x-axis direction, the y-axis direction, and the z-axis direction in the plurality of regions, identify a plurality of features based on the centers and variances of the clusters, and concatenate the plurality of features into one or more concatenated features. The classification module is configured to receive the one or more concatenated features and declare at least one of a near state, an internal state, and an unobstructed state of the tunnel in response to the one or more concatenated features.

Inventors

• S. Jintalapudi
• B. N.R. Buckkos
• T. Elvitigara

Assignees

• 通用汽车环球科技运作有限责任公司

Dates

Publication Date

20260508

Application Date

20241227

Priority Date

20241101

Claims (10)

1. 1. A driver assistance system for a vehicle, comprising: a light detection and ranging (LiDAR) sensor configured to transmit light pulses and receive echoes, and A detection module configured to detect a tunnel in a path of the vehicle, the detection module comprising: a zoning module configured to bin echoes from the LiDAR sensor into a plurality of zones; a cluster and feature extraction module configured to: The clusters in the region are identified and, Determining the center and variance of the cluster in the x-axis direction, y-axis direction and z-axis direction in the plurality of regions, Identifying a plurality of features based on the center and variance of the cluster, and Concatenating the plurality of features into one or more concatenated features, and A classification module configured to receive the one or more concatenated features and declare at least one of a near state, an internal state, and an unobstructed state of 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 includes a first zone and a second zone.
3. 3. The driver assistance system of claim 2, wherein the first zone corresponds to a return from the LiDAR sensor having a value in the z-axis direction that is less than a predetermined height, and the second zone corresponds to a return having a value in the z-axis direction that is greater than the predetermined height.
4. 4. The driver assistance system of claim 1, further comprising a Global Positioning System (GPS), wherein the LiDAR sensor converts the return to a Frenet 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 for the return in response to the pitch of the vehicle.
6. 6. The driver assistance system of claim 3, wherein the classifier module comprises a pre-trained model configured to detect the approaching state, the internal state, and the unobstructed 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 unfeasible state transitions.
9. 9. The driver assistance system of claim 6, wherein the pre-trained model is configured to: Detecting the proximity state in response to the variance of clusters in the x-axis direction in the second region being less than the first variance and the variance of clusters in the z-axis direction in the second region being greater than the second variance, and The internal state is detected in response to a variance of clusters in an x-axis direction in the second region being greater than a third variance and a variance of clusters in a z-axis direction in the second region 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 variances of clusters in the x-axis direction and the z-axis direction.

Description

Detection and classification of tunnels using light detection and ranging Technical Field 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 that include light detection and ranging (LiDAR) sensors. Background Vehicles that include various levels of driver assistance, such as fully or partially autonomous vehicles, typically rely on radio detection and ranging (radar) systems to detect and avoid objects in the path of the vehicle. Radar-based systems encounter problems in detecting objects as the vehicle travels through a tunnel (or other similar infrastructure, such as under an overpass). For example, radar-based systems detect phantom objects and/or false trajectories caused by tunnel infrastructure while moving through a tunnel. Disclosure of Invention A driver assistance system for a vehicle includes a light detection and ranging (LiDAR) sensor configured to transmit light pulses and receive echoes. The detection module is configured to detect a tunnel in a path of the vehicle. The detection module includes a zoning module configured to bin echoes from LiDAR sensors into a plurality of zones. The detection module includes a cluster and feature extraction module configured to identify clusters in the region, determine centers and variances of the clusters in the x-axis direction, the y-axis direction, and the z-axis direction in the plurality of regions, identify a plurality of features based on the centers and variances of the clusters, and concatenate the plurality of features into one or more concatenated features. The classification module is configured to receive the one or more concatenated features and declare at least one of a near state, an internal state, and an unobstructed state of the tunnel in response to the one or more concatenated features. In other features, the plurality of regions includes a first region and a second region. The first zone corresponds to a return from the LiDAR sensor having a value in the z-axis direction that is less than a predetermined height. The second zone corresponds to an echo having a value in the z-axis direction greater than a predetermined height. The driver assistance system includes a Global Positioning System (GPS). LiDAR sensors convert echoes to Frenet frames in response to data from the GPS system. In other features, the inertial measurement system is configured to detect a trim of the vehicle, wherein the LiDAR sensor compensates for return in response to the trim of the vehicle. The classifier module includes a pre-trained model configured to detect a proximity state, an internal state, and an unobstructed state in response to one or more concatenated features. The filter module is configured to filter an output of the classifier module using a hidden Markov model (Hidden Markov Model). Hidden markov models filter out unfeasible state transitions. In other features, the pre-trained model is configured to detect the approaching state in response to the variance of the clusters in the x-axis direction in the second region being less than the first variance and the variance of the clusters in the z-axis direction in the second region being greater than the second variance, and to detect the internal state in response to the variance of the clusters in the x-axis direction in the second region being greater than the third variance and the variance of the clusters in the z-axis direction in the second region being less than the 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 variances of the clusters in the x-axis direction and the z-axis direction. A method for assisting a driver of a vehicle includes transmitting light pulses and receiving echoes using a light detection and ranging (LiDAR) sensor, binning the echoes from the LiDAR sensor into a plurality of zones, identifying clusters in the zones, determining centers and variances of the clusters in an x-axis direction, a y-axis direction, and a z-axis direction in the plurality of zones, identifying a plurality of features based on the centers and variances, concatenating the plurality of features into one or more concatenated features, and detecting at least one of a near state, an internal state, and an unobstructed state of a tunnel in response to the one or more concatenated features. In other features, the plurality of regions includes a first regio