Hybrid Resilient Framework for Self-Driving Car

Engineering notes

Experimental results using the CARLA simulator demonstrate that our model consistently outperforms existing approaches in runtime efficiency while maintaining functional integrity under attack. Time-based performance evaluations reveal that our hybrid model achieves faster and more reliable outputs, validating its effectiveness in resilient autonomous perception.

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

Self-driving cars are expected to become a convenient source of transportation in near future. However, achieving a self-driving car at Level 5 still poses challenges. The challenges could be attributed to maintaining desirable functionality under uncertainty while accomplishing a task. This paper proposes a hybrid N-redundancy-based resilient framework for the perception module that integrates abstract sensor fusion and a probabilistic deep learning model to ensure resilient performance. Experimental results using the CARLA simulator demonstrate that our model consistently outperforms existing approaches in runtime efficiency while maintaining functional integrity under attack. Time-based performance evaluations reveal that our hybrid model achieves faster and more reliable outputs, validating its effectiveness in resilient autonomous perception.

Links and sources

• Official / arXiv page
• PDF from original source

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