Integration of MoS2 Memtransistor Devices and Analogue Circuits for Sensor Fusion in Autonomous Vehicle Target Localization.

Abstract

In contemporary autonomous driving systems relying on sensor fusion, traditional digital processors encounter challenges associated with analogue-to-digital conversion and iterative vector-matrix operations, which are encumbered by limitations in terms of response time and energy consumption. In this study, we present an analogue Kalman filter circuit based on molybdenum disulfide (MoS2) memtransistor, designed to accelerate sensor fusion for precise localization in autonomous vehicle applications. The nonvolatile memory characteristics of the memtransistor allow for the storage of a fixed Kalman gain, which eliminates the data convergence and thus accelerates the processing speeds. Additionally, the modulation of multiple conductance states by the gate terminal enables fast adaptability to diverse autonomous driving scenarios by tuning multiple Kalman filter gains. Our proposed analogue Kalman filter circuit accurately estimates the position coordinates of target vehicles by fusing sensor data from light detection and ranging (LiDAR), millimeter-wave radar (Radar), and camera, and it successfully solves real-word problems in a signal-free crossroad intersection. Notably, our system achieves a 1000-fold improvement in energy efficiency compared to that of digital circuits. This work underscores the viability of a memtransistor for achieving fast, energy-efficient real-time sensing, and continuous signal processing in advanced sensor fusion technology.