Comprehensive and Practical Vision System for Self-Driving Vehicle Lane-Level Localization

Abstract

Vehicle lane-level localization is a fundamental technology in autonomous driving. To achieve accurate and consistent performance, a common approach is to use the LIDAR technology. However, it is expensive and computational demanding, and thus not a practical solution in many situations. This paper proposes a stereovision system, which is of low cost, yet also able to achieve high accuracy and consistency. It integrates a new lane line detection algorithm with other lane marking detectors to effectively identify the correct lane line markings. It also fits multiple road models to improve accuracy. An effective stereo 3D reconstruction method is proposed to estimate vehicle localization. The estimation consistency is further guaranteed by a new particle filter framework, which takes vehicle dynamics into account. Experiment results based on image sequences taken under different visual conditions showed that the proposed system can identify the lane line markings with 98.6% accuracy. The maximum estimation error of the vehicle distance to lane lines is 16 cm in daytime and 26 cm at night, and the maximum estimation error of its moving direction with respect to the road tangent is 0.06 rad in daytime and 0.12 rad at night. Due to its high accuracy and consistency, the proposed system can be implemented in autonomous driving vehicles as a practical solution to vehicle lane-level localization.

Perception, Planning, Control, and Coordination for Autonomous Vehicles

“…As summarized in the survey paper by Hillel et al in [68], most of the lane line detection algorithms share three common steps: (1) lane line feature extraction, by edge detection [76,77] and color [78,79], by learning algorithms such as SVM [80], or by boost classification [81,82]; (2) fitting the pixels into different models, e.g., straight lines [83,84], parabolas [85,86], hyperbolas [87][88][89], and even zigzag line [90]; (3) estimating the vehicle pose based on the fitted model. A fourth time integration step may exist before the vehicle pose estimation in order to impose temporal continuity, where the detection result in the current frame is used to guide the next search through filter mechanisms, such as Kalman filter [76,91] and particle filter [80,90,92].…”

“…A fourth time integration step may exist before the vehicle pose estimation in order to impose temporal continuity, where the detection result in the current frame is used to guide the next search through filter mechanisms, such as Kalman filter [76,91] and particle filter [80,90,92].…”

“…As the viewing distance becomes larger, the interpolation becomes more and more inaccurate. To solve this problem, Du et al [90] provide an adaptive mechanism to update the step width online based on the previous width measurements.…”

“…Depending on the model used, the vehicle pose can be derived from the fitted model as shown in [88,95]. The model can also be used to guide the lane line detection in the next frame to improve continuity (e.g., [90,103,104]). …”

“…In [85], each particle represents the location of the vehicle in real world coordinates, but again the motion of vehicle is simply assumed to be Gaussian. Since the change between consecutive images is purely due to the vehicle motion, Du et al [90] provided a more intuitive and straightforward approach which applied the particle filter to the moving vehicle and took its explicit dynamic model into account.…”