Redundant lane detection for autonomous vehicles
Systems and methods of automatic correction of map data for autonomous vehicle navigation are disclosed. An autonomous vehicle system can receive first sensor data from a first sensor of an autonomous vehicle and second sensor data from a second sensor of the autonomous vehicle, the first sensor data and the second sensor data captured during operation of the autonomous vehicle; generate, based on the first sensor data, a first prediction of a dimension of a lane of a road; generate, based on the second sensor data, a second prediction of a position of the autonomous vehicle within the lane of the road; determine a confidence value for the second prediction of the position of the autonomous vehicle within the lane of the road based on the first prediction and the second prediction; and navigate the autonomous vehicle based on the confidence value.
1 . A method, comprising:
receiving, by one or more processors coupled to non-transitory memory, first sensor data from a first sensor of an autonomous vehicle and second sensor data from a second sensor of the autonomous vehicle, the first sensor data and the second sensor data captured during operation of the autonomous vehicle;
generating, by the one or more processors, based on the first sensor data, a first prediction of a dimension of a lane of a road by executing a first artificial intelligence model;
generating, by the one or more processors, based on the second sensor data, a second prediction of a position of the autonomous vehicle within the lane of the road by executing a second artificial intelligence model different from the first artificial intelligence model;
determining, by the one or more processors, a confidence value for the second prediction of the position of the autonomous vehicle within the lane of the road based on the first prediction and the second prediction; and
navigating, by the one or more processors, the autonomous vehicle based on the confidence value.
2 . The method of claim 1 , wherein the first sensor data comprises light detection and ranging (LiDAR) data and the second sensor data comprises image data.
3 . The method of claim 1 , wherein the first artificial intelligence model is trained to generate the first prediction using the first sensor data as input.
4 . The method of claim 3 , wherein the second artificial intelligence model is trained to generate the second prediction using the second sensor data as input.
5 . The method of claim 4 , wherein the first artificial intelligence model comprises a regression model and the second artificial intelligence model comprises a neural network.
6 . The method of claim 1 , further comprising:
generating, by the one or more processors, a third prediction of the position of the autonomous vehicle within the lane of the road based on the first sensor data; and
determining, by the one or more processors, the confidence value for the second prediction of the position of the autonomous vehicle within the lane of the road further based on the third prediction.
7 . The method of claim 1 , further comprising:
executing, by the one or more processors, a segmentation algorithm using the first sensor data or the second sensor data to generate a segmentation of the first sensor data; and
determining, by the one or more processors, the confidence value for the second prediction of the position of the autonomous vehicle within the lane of the road further based on the segmentation.
8 . The method of claim 1 , wherein the first prediction comprises a prediction of a width of the lane.
9 . The method of claim 1 , wherein the second prediction comprises a distance from a left lane line, a distance from a right lane line, or a distance from a center of the lane.
10 . The method of claim 1 , further comprising determining, by the one or more processors, based on the first sensor data or the second sensor data, a lane offset for the autonomous vehicle.
11 . A system, comprising:
one or more processors coupled to non-transitory memory, the one or more processors configured to:
receive first sensor data from a first sensor of an autonomous vehicle and second sensor data from a second sensor of the autonomous vehicle, the first sensor data and the second sensor data captured during operation of the autonomous vehicle;
generate, based on the first sensor data, a first prediction of a dimension of a lane of a road by executing a first artificial intelligence model;
generate, based on the second sensor data, a second prediction of a position of the autonomous vehicle within the lane of the road by executing a second artificial intelligence model different from the first artificial intelligence model;
determine a confidence value for the second prediction of the position of the autonomous vehicle within the lane of the road based on the first prediction and the second prediction; and
navigate the autonomous vehicle based on the confidence value.
12 . The system of claim 11 , wherein the first sensor data comprises light detection and ranging (LiDAR) data and the second sensor data comprises image data.
13 . The system of claim 11 , wherein the first artificial intelligence model is trained to generate the first prediction using the first sensor data as input.
14 . The system of claim 13 , wherein the second artificial intelligence model is trained to generate the second prediction using the second sensor data as input.
15 . The system of claim 14 , wherein the first artificial intelligence model comprises a regression model and the second artificial intelligence model comprises a neural network.
16 . The system of claim 11 , wherein the one or more processors are further configured to:
generate a third prediction of the position of the autonomous vehicle within the lane of the road based on the first sensor data; and
determine the confidence value for the second prediction of the position of the autonomous vehicle within the lane of the road further based on the third prediction.
17 . The system of claim 11 , wherein the one or more processors are further configured to:
execute a segmentation algorithm using the first sensor data or the second sensor data to generate a segmentation of the first sensor data; and
determine the confidence value for the second prediction of the position of the autonomous vehicle within the lane of the road further based on the segmentation.
18 . The system of claim 11 , wherein the first prediction comprises a prediction of a width of the lane.
19 . The system of claim 11 , wherein the second prediction comprises a distance from a left lane line, a distance from a right lane line, or a distance from a center of the lane.
20 . The system of claim 11 , wherein the one or more processors are further configured to determine, based on the first sensor data or the second sensor data, a lane offset for the autonomous vehicle.