State-of-the-art lane detection methods use a variety of deep learning techniques for lane feature extraction and prediction, demonstrating better performance than conventional lane detectors. However, deep learning approaches are computationally demanding and often fail to meet real-time requirements of autonomous vehicles. This paper proposes a lane detection method using a light-weight convolutional neural network model as a feature extractor exploiting the potential of deep learning while meeting real-time needs. The developed model is trained with a dataset containing small image patches of dimension 16 × 64 pixels and a non-overlapping sliding window approach is employed to achieve fast inference. Then, the predictions are clustered and fitted with a polynomial to model the lane boundaries. The proposed method was tested on the KITTI and Caltech datasets and demonstrated an acceptable performance. We also integrated the detector into the localization and planning system of our autonomous vehicle and runs at 28 fps in a CPU on image resolution of 768 × 1024 meeting real-time requirements needed for self-driving cars.
Towards Driving-Oriented Metric for Lane Detection Models
After the 2017 TuSimple Lane Detection Challenge, its dataset and evaluation based on accuracy and F1 score have become the de facto standard to measure the performance of lane detection methods. While they have played a major role in improving the performance of lane detection methods, the validity of this evaluation method in downstream tasks has not been adequately researched. In this study, we design 2 new driving-oriented metrics for lane detection: End-to-End Lateral Deviation metric (E2E-LD) is directly formulated based on the requirements of autonomous driving, a core downstream task of lane detection; Per-frame Simulated Lateral Deviation metric (PSLD) is a lightweight surrogate metric of E2E-LD. To evaluate the validity of the metrics, we conduct a large-scale empirical study with 4 major types of lane detection approaches on the TuSimple dataset and our newly constructed dataset Comma2k19-LD. Our results show that the conventional metrics have strongly negative correlations (≤-0.55) with E2E-LD, meaning that some recent improvements purely targeting the conventional metrics may not have led to meaningful improvements in autonomous driving, but rather may actually have made it worse by overfitting to the conventional metrics. As autonomous driving is a security/safety-critical system, the underestimation of robustness hinders the sound more »
- Publication Date:
- NSF-PAR ID:
- 10359468
- Journal Name:
- IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR)
- Sponsoring Org:
- National Science Foundation
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