Beyond cameras and LiDAR, several other sensor modalities could benefit from the proposed multi-task learning framework for robust perception in autonomous vehicles. These include:
Radar: Radar sensors are particularly effective in adverse weather conditions and can provide valuable information about the speed and distance of objects. Integrating radar data with the existing framework could enhance the robustness of object detection, especially in scenarios where visual data is compromised.
Ultrasonic Sensors: Commonly used for close-range detection, ultrasonic sensors can provide additional depth information in parking and low-speed maneuvers. Incorporating these sensors into the multi-task learning framework could improve the vehicle's ability to detect nearby obstacles.
Inertial Measurement Units (IMUs): IMUs provide critical data on the vehicle's orientation and acceleration. By integrating IMU data, the framework could enhance the accuracy of sensor fusion, particularly in dynamic environments where rapid changes in position and orientation occur.
GPS: While GPS provides geolocation data, its accuracy can be affected by environmental factors. Incorporating GPS data into the multi-task learning framework could help in improving the overall situational awareness of the vehicle, especially in conjunction with other sensor modalities.
By leveraging the strengths of these additional sensor modalities within the multi-task learning framework, the perception system can achieve a more comprehensive understanding of the vehicle's environment, leading to improved safety and reliability in autonomous driving applications.