| Autor: |
Prasanna Kumar, Rahul, Melcher, David, Buttolo, Pietro, Jia, Yunyi |
| Zdroj: |
IEEE Transactions on Intelligent Transportation Systems; 2023, Vol. 24 Issue: 7 p6800-6819, 20p |
| Abstrakt: |
Autonomous vehicles (AV) are a promising contemporary engineering innovation. Since they have no human drivers, their vehicle controllers must be fully informed of the activities of their occupants in vehicle seats so that occupant safety and comfort can be guaranteed. This paper introduces a system that employs capacitive sensing and machine learning to inform the controller of occupant activities, like actions and posture changes. The system facilitates capacitive sensing by deploying a capacitance-sensing mat on the vehicle seat. A sensing circuitry connected to the mat measures all its capacitances continuously. Since an occupant’s body induces variations in these capacitances, temporal sequences of capacitance measurements represent various occupant activities in the vehicle seat. Subsequently, the system converts capacitance measurements corresponding to every time step to two grayscale capacitance-sensing images (CSIs). The CSIs, in turn, yield a feature vector corresponding to every time step, thereby building a dataset of temporal sequences of features. The system’s machine-learning unit tracks and recognizes occupant actions and posture changes using an action-recognition block, which is essentially a long short-term memory (LSTM) network trained on a dataset of temporal sequences of either features or capacitance measurements corresponding to various occupant actions. If the occupant remains stationary, a switching block in the unit disables the action-recognition block and enables a posture-recognition block. The latter is a ${k}$ -nearest-neighbor ( ${k}$ NN) classifier trained on a dataset of features to recognize stationary occupant postures. This paper validates the system’s performance and investigates its deployment for real-time tracking of occupant activities in AVs. |
| Databáze: |
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