Real Time Steering Angle Estimation Using Monocular Depth Profiling and 1D Free Space Analysis

Engineering notes

Experimental results demonstrate that our approach achieves comparable performance to more complex systems while operating at 10–15 frames per second on consumer hardware.

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

Autonomous navigation systems traditionally rely on expensive sensor arrays such as LiDAR or stereo cameras to perceive their environment and make steering decisions. We propose a novel, cost effective approach that uses a monocular camera and deep learning-based depth estimation to generate steering commands in real time. Our method employs the Depth-Anything-V2 model to create depth maps from single RGB images, followed by a computationally efficient 1D free space analysis algorithm that identifies navigable paths. By analyzing a region of interest in the lower portion of the frame and computing a 1D profile of the depth information, we determine optimal steering angles with minimal computational overhead. Experimental results demonstrate that our approach achieves comparable performance to more complex systems while operating at 10–15 frames per second on consumer hardware. The system shows particular promise for low cost autonomous vehicles, mobile robots, and advanced driver assistance systems where traditional depth sensors are impractical due to cost, size, or power constraints.

Links and sources

• Official / arXiv page

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