The process of embedded vision application development typically consists of five steps, shown in Figure 1.
In this white paper, we briefly touch on the characteristics and challenges of lane-detection algorithms. Then we turn our focus to the implementation and optimization of the algorithm in steps 3 and 4, using the Tensilica Vision DSP and the corresponding DSP-optimized CV library, XI library.
Implementing a Robust ADAS Lane-Detection Algorithm
The lane-departure warning system (LDWS) has been an essential function to the realization of an ADAS system for autonomous driving. Almost every LDWS starts with a lane-mark-detection phase that can be generalized and simplified with the following key steps:
- Road feature extraction
- Post-processing for outlier removal
- Tracking filtering and data fusion
The accuracy and reliability of LDWS depends on the accuracy and robustness of the lane-detection algorithm, which must take into consideration the shapes of the lane marks, non-uniform texture on the road surface, lighting conditions, shadows and obstructions, etc., while computing in real time following the high-speed movement of the vehicle. In this paper, we present a robust lane-mark-detection algorithm that deploys multiple CV processing steps to enhance the robustness of the detection.