Incorporating additional modalities like LiDAR point clouds and vehicle dynamics data into MiniDrive could significantly enhance its performance in autonomous driving tasks. LiDAR data provides precise spatial information about the environment, allowing for a more accurate representation of the surrounding objects and their distances from the ego vehicle. By integrating LiDAR point clouds, MiniDrive could improve its perception capabilities, particularly in complex scenarios where visual data alone may be insufficient, such as in low-light or adverse weather conditions.
To effectively integrate LiDAR data, a multimodal approach could be employed, where the LiDAR point clouds are processed alongside the visual inputs. This could involve using a dedicated LiDAR encoder that extracts relevant features from the point clouds, which are then fused with the visual features extracted by the UniRepLKNet backbone. The Feature Engineering Mixture of Experts (FE-MoE) module could be adapted to handle this additional data stream, allowing for a more comprehensive understanding of the environment.
Furthermore, incorporating vehicle dynamics data, such as speed, acceleration, and steering angles, could enhance MiniDrive's decision-making capabilities. This data could be used to inform the model about the vehicle's current state and its potential future trajectories, enabling it to make more informed predictions and planning decisions. By integrating these additional modalities, MiniDrive could achieve a more holistic understanding of the driving environment, leading to improved safety and reliability in autonomous driving scenarios.