Autonomous driving paper index
Self-rectifying memristors with high rectification ratio for attack-resilient autonomous driving systems
One-line summary
With the rise of big data and the Internet of Things, smart devices, especially autonomous driving systems, have become prime targets for information leakage and cyberattacks.
Engineering notes
Key topics: autonomous driving system, autonomous driving. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
With the rise of big data and the Internet of Things, smart devices, especially autonomous driving systems, have become prime targets for information leakage and cyberattacks. This study presents the design and fabrication of a self-rectifying memristor utilizing a TiN/HfOx/Pt structure to enhance the security and reliability of autopilot systems. Following rapid thermal annealing treatment, the self-rectifying memristor demonstrates a recorded rectification ratio exceeding 108 and a nonlinearity of over 105, coupled with minimal device-to-device (3.32%) and cycle-to-cycle variations (1.55%). We further extend the application of self-rectifying memristors into crossbar arrays for the real-time classification of autonomous driving datasets, showcasing their capability to execute artificial neural networks at the hardware level. The proposed crossbar arrays exhibit robust attack resilience, achieving classification accuracy (84.25%) comparable to those of software models (84.34%), particularly under complex attack scenarios. This work not only highlights the potential of self-rectifying memristors in bolstering the security of autonomous driving systems but also offers innovative strategies for safeguarding future intelligent transportation systems. This study presents a self-rectifying memristor (SRM) with high rectification ratio based on a TiN/HfOx/Pt structure. It demonstrates minimal device-to-device and cycle-to-cycle variations, enabling scalability and suitability for in-memory computing. SRM-based crossbar arrays achieve software-comparable accuracy and show promise to enhance autonomous driving security and reliability.
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