Abstract: An apparatus includes at least one camera configured to capture a series of image frames for traffic lanes in front of an ego vehicle, where each of the series of image frames is captured at a different one of a plurality of times. A target object detection and tracking controller is configured to process each of the image frames using pixel measurements extracted from the respective image frame to determine, from the pixel measurements, a predicted time to line crossing for a target vehicle detected in the respective image frame at a time corresponding to capture of the respective image frame.

Abstract: An apparatus includes at least one camera configured to capture an image of a traffic lane in front of a vehicle. The apparatus also includes a vehicle behavior prediction controller configured to determine lane boundaries and road curvature for a segment of a traffic lane occupied by the vehicle from the captured image and prior captured images; determine lateral distances of the vehicle from the lane boundaries and a rate of departure of the vehicle from the occupied traffic lane that is accurate for the determined road curvature; determine a time to line crossing for the vehicle from the lateral distances and the rate of departure; and activate a lane departure warning indicator based on the determined time to line crossing.

Abstract: An apparatus includes at least one camera configured to capture at least one image of a traffic lane, an inertial measurement unit (IMU) configured to detect motion characteristics, and at least one processor. The at least one processor is configured to obtain a vehicle motion trajectory using the IMU and based on one or more vehicle path prediction parameters, obtain a vehicle vision trajectory based on the at least one image, wherein the vehicle vision trajectory includes at least one lane boundary, determine distances between one or more points on the vehicle and one or more intersection points of the at least one lane boundary based on the obtained vehicle motion trajectory, determine at least one time to line crossing (TTLC) based on the determined distances and a speed of the vehicle, and activate a lane departure warning indicator based on the determined at least one TTLC.

Abstract: A method includes obtaining sensor data associated with a target object at an ego vehicle, where the sensor data includes image data within an image frame. The method also includes identifying a first longitudinal distance of a first point of the target object from the ego vehicle within a world frame. The method further includes identifying a first lateral distance of the first point of the target object from the ego vehicle within the world frame using a first normalization ratio that is based on the image data. The method also includes predicting a future path of the target object based on the first longitudinal distance and the first lateral distance. In addition, the method includes controlling at least one operation of the ego vehicle based on the predicted future path of the target object.

Abstract: A method includes identifying one or more edge cases associated with at least one trained machine learning model, where the at least one trained machine learning model is configured to perform at least one function related to one or more vehicles. The method also includes obtaining raw data associated with the one or more edge cases from at least one of the one or more vehicles and selecting a subset of the raw data. The method further includes generating synthetic data associated with the one or more edge cases. In addition, the method includes at least one of: retraining the at least one trained machine learning model and training at least one new machine learning model using the selected subset of raw data and the synthetic data.

Abstract: An apparatus includes an inertial measurement unit (IMU) configured to detect motion characteristics of a vehicle. The apparatus also includes an odometry system configured to detect a wheel speed of each wheel of the vehicle. The apparatus further includes at least one processor communicatively connected to the IMU and the odometry system, the at least one processor configured to determine first parameters for predicting a path of the vehicle, determine second parameters for predicting the path of the vehicle, and predict the path of the vehicle using a combination of the first parameters and the second parameters, wherein the combination is weighted based on a longitudinal acceleration of the vehicle obtained using the IMU.

Abstract: A method includes obtaining sensor data associated with a target object at an ego vehicle, where the sensor data includes image data within an image frame. The method also includes identifying a first longitudinal distance of a first point of the target object from the ego vehicle within a world frame. The method further includes identifying a first lateral distance of the first point of the target object from the ego vehicle within the world frame using a first normalization ratio that is based on the image data. The method also includes predicting a future path of the target object based on the first longitudinal distance and the first lateral distance. In addition, the method includes controlling at least one operation of the ego vehicle based on the predicted future path of the target object.

Abstract: A method includes obtaining first and second data captured using different types of sensors. The method also includes obtaining first object detection results based on the first data and generated using a machine learning model, where the first object detection results identify one or more objects detected using the first data. The method further includes obtaining second object detection results based on the second data, where the second object detection results identify one or more objects detected using the second data. The method also includes identifying one or more inconsistencies between the first and second object detection results and generating labeled training data based on the one or more identified inconsistencies. In addition, the method includes retraining the machine learning model or training an additional machine learning model using the labeled training data.

Abstract: An apparatus includes at least one camera configured to capture at least one image of a traffic lane, an inertial measurement unit (IMU) configured to detect motion characteristics, and at least one processor. The at least one processor is configured to obtain a vehicle motion trajectory using the IMU and based on one or more vehicle path prediction parameters, obtain a vehicle vision trajectory based on the at least one image, wherein the vehicle vision trajectory includes at least one lane boundary, determine distances between one or more points on the vehicle and one or more intersection points of the at least one lane boundary based on the obtained vehicle motion trajectory, determine at least one time to line crossing (TTLC) based on the determined distances and a speed of the vehicle, and activate a lane departure warning indicator based on the determined at least one TTLC.

Abstract: A method includes obtaining an image of a scene and identifying one or more labels for one or more objects captured in the image. The method also includes generating one or more domain-specific augmented images by modifying the image, where the one or more domain-specific augmented images are associated with the one or more labels. In addition, the method includes training or retraining a machine learning model using the one or more domain-specific augmented images and the one or more labels. Generating the one or more domain-specific augmented images may include at least one of modifying the image to include a different amount of motion blur, modifying the image to include a different lighting condition, and modifying the image to include a different weather condition.

Abstract: An apparatus includes at least one camera configured to capture an image of a traffic lane in front of a vehicle. The apparatus also includes a radar transceiver configured to detect one or more target vehicles proximate to the vehicle. The apparatus further includes a path prediction and vehicle detection controller configured to determine first parameters for predicting a path of the vehicle; determine second parameters for predicting the path of the vehicle; predict the path of the vehicle using a combination of the first parameters and the second parameters, where the combination is weighted based on a speed of the vehicle; identify one of the one or more target vehicles as a closest in path vehicle based on the predicted path of the vehicle; and activate at least one of a braking control and a steering control based on a proximity of the identified closest in path vehicle.

Abstract: An apparatus includes at least one camera configured to capture an image of a traffic lane in front of a vehicle. The apparatus also includes a vehicle behavior prediction controller configured to determine lane boundaries and road curvature for a segment of a traffic lane occupied by the vehicle from the captured image and prior captured images; determine lateral distances of the vehicle from the lane boundaries and a rate of departure of the vehicle from the occupied traffic lane that is accurate for the determined road curvature; determine a time to line crossing for the vehicle from the lateral distances and the rate of departure; and activate a lane departure warning indicator based on the determined time to line crossing.

Abstract: An apparatus includes at least one camera configured to capture a series of image frames for traffic lanes in front of an ego vehicle, where each of the series of image frames is captured at a different one of a plurality of times. A target object detection and tracking controller is configured to process each of the image frames using pixel measurements extracted from the respective image frame to determine, from the pixel measurements, a predicted time to line crossing for a target vehicle detected in the respective image frame at a time corresponding to capture of the respective image frame.