Canoo Technologies Inc.

Aviral K Singh

Kilsoo Kim

Jongmoo Choi

David R Arft

Mayukh Sattiraju

Siddharth Agarwal

Dheemanth Uppalapati

Paolo E Pucci

Lei Cao

Lung En Jan

Vishal U Shanbhag

Shanmukha M Bhumireddy

Dhruvil Darji

Emilio Moyers

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.

System and method in vehicle path prediction based on odometry and inertial measurement unit

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.

System and method in lane departure warning with full nonlinear kinematics and curvature

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 path tracking controller configured to detect lane boundaries and a path curvature for the traffic lane from the image, determine a lateral offset of the vehicle from a reference path for the traffic lane and a heading offset for the vehicle from the path curvature, determine a yaw rate maintaining the vehicle within the traffic lane using a kinematics control, determine a steering angle maintaining the vehicle within the traffic lane using a dynamics control and the yaw rate determined by the kinematics control, and activate a steering control based on the determined steering angle.

System and methods of integrating vehicle kinematics and dynamics for lateral control feature at autonomous driving

A method includes identifying a path to be followed by an ego vehicle. The method also includes determining a desired yaw rate and a desired yaw acceleration for the ego vehicle based on the identified path. The method further includes determining a desired yaw moment for the ego vehicle based on the desired yaw rate and the desired yaw acceleration. In addition, the method includes distributing the desired yaw moment to multiple wheels of the ego vehicle such that the distributed desired yaw moment creates lateral movement of the ego vehicle during travel along the identified path. In some cases, the desired yaw rate and the desired yaw acceleration for the ego vehicle may be determined based on nonlinear kinematics of the ego vehicle, and the desired yaw moment for the ego vehicle may be determined based on a single-track dynamic model of the ego vehicle.

Autonomous lateral control of vehicle using direct yaw moment control

A method includes determining a desired yaw moment to be applied to an ego vehicle during travel. The method also includes identifying yaw moment changes that are achievable using different torque vectoring techniques supported by the ego vehicle. The method further includes selecting at least one of the torque vectoring techniques based on the identified yaw moment changes. In addition, the method includes using the at least one selected torque vectoring technique to obtain the desired yaw moment and create lateral movement of the ego vehicle during the travel. In some cases, a desired response time associated with the lateral movement of the ego vehicle may be used, where steering control provides a faster response time and torque vectoring control provides a slower response time. The at least one torque vectoring technique may be selected based on different energy efficiencies associated with different ones of the torque vectoring techniques.

Path tracking control for self-driving of vehicle with yaw moment distribution

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.

Prediction of target object's behavior based on world and image frames

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.

System and method for lane departure warning with ego motion and vision

Operation and motion control, by a vehicle's ADAS or AD features, is improved in ways suitable to EVs having higher driving and handling performance. The vehicle dynamic model for high rates of lateral acceleration (e.g., sharp cornering or taking curves having a small radius of curvature as faster speeds) is improved by one or more of optimizing time cornering stiffness with a sigmoid function and/or altering front/rear steering angle to account for roll steer and compliance steer, based on vehicle testing. Indicators for lane departure warning or collision warning, evasive steering, or emergency braking are therefore reliably extended to allow higher performance maneuvers.

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Torrance, CA, United States of America

Aviral K Singh