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首页复杂道路环境下智能车辆的精确车道检测算法
本文主要探讨了"智能车辆复杂道路与动态环境下车道检测算法"这一关键领域。随着智能交通的发展,准确的车道检测是实现自动驾驶和高级驾驶辅助系统(Autonomous Driving Assistance Systems, ADAS)的基础。传统的车道检测方法在精度上可能存在不足,而基于深度学习的方法在实时性能方面可能存在挑战。为了克服这些问题,研究人员提出了一个新的算法。 首先,该算法针对图像扭曲的问题,通过使用叠加阈值算法进行边缘检测,将原始图像转换成一个平滑的视图。接着,通过感兴趣区域的提取和反向透视变换,从鸟瞰视角清晰地展示车道区域,这有助于减少噪声干扰和提高定位准确性。 其次,采用随机样本一致(RANSAC)算法结合三阶B样条曲线模型,对车道线进行拟合。RANSAC是一种强大的模型选择算法,即使在存在大量噪声的情况下也能找到最优的曲线参数。通过拟合,作者能够计算出车道曲线的精确度和曲率半径,从而进一步优化车道识别的精度。 实验部分,研究者利用在复杂路况下采集的驾驶视频数据以及TuSimple数据集进行了模拟测试。结果显示,对于道路驾驶视频,该算法的平均检测精度达到98.49%,处理时间仅需21.5毫秒,显示出极高的实时性。而在TuSimple数据集上的检测精度同样高达98.42%,平均处理时间为22.2毫秒,证明了算法在多变环境中的稳定性和高效性。 相较于传统方法和深度学习方法,该算法在保持高精度的同时,显著提升了处理速度,具有较强的抗干扰能力。这对于提升智能车辆驾驶辅助技术的水平、保障驾驶安全具有重要意义。因此,这个算法对于推动智能交通技术的发展和提高智能车辆驾驶安全性具有深远的影响。
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Sensors 2019, 19, 3166 5 of 21
Define
α
x
= f /dX
, which represent the ratio of focal length f to the physical dimension of a pixel
in the u-axis direction. Define
α
y
= f /dY
, which represent the ratio of focal length f to the physical
dimension of a pixel in the v-axis direction. Then, the above formula can be equivalent to:
Z
c
u
v
1
=
α
x
0 u
0
0
0 α
y
v
0
0
0 0 1 0
"
R t
0
T
1
#
X
w
Y
w
Z
w
1
= M
1
M
2
X
w
Y
w
Z
w
1
(2)
M
1
is a 3
×
4 matrix determined by parameters such as
α
x
,
α
y
, u
0
and v
0
. These four parameters
are related to the internal structure of the camera called the internal parameters of the camera. The
rotation matrix R and the translation vector t are determined by the orientation relationship between
the camera and the world coordinates. Therefore, M
2
is called the external parameters of the camera.
The internal and external parameters of the camera were obtained through multiple experiments and
calculations, and the process of camera calibration was completed.
2.2. Image Distortion Removal
In recent years, vehicle-mounted cameras often used wide-angle lens to obtain an enlarged
field of vision, and the image distortion changes appeared remarkable. Image distortion is generally
divided into radial and tangential distortions. Compared with the actual road scene, the common
edge distortion of lanes, vehicles and backgrounds is called radial distortion. If the camera lens is not
parallel to the image plane of the visual sensor, the collected image will be tilted, thus making the lane,
vehicle and background look closer or farther than in the actual three-dimensional world. This type
of image distortion is called tangential distortion, which is more disadvantageous to reflect the real
driving environment.
The mathematical model of radial distortion is defined as follows:
(
u
0
= u(1 + k
1
r
2
+ k
2
r
4
+ k
3
r
6
)
v
0
= v(1 + k
1
r
2
+ k
2
r
4
+ k
3
r
6
)
(3)
where, r
2
= x
2
+ y
2
, (u
0
,v
0
) is the coordinate of point d in the Image Pixel Coordinate.
The mathematical model of tangential distortion is presented as follows:
u
0
= u +
h
2p
1
v + p
2
(r
2
+ 2u
2
)
i
v
0
= v +
h
p
1
(r
2
+ 2v
2
) + 2p
2
u
i
(4)
Equations (3) and (4) demonstrate that image distortion is related to five parameters—k
1
, k
2
,
k
3
, p
1
and p
2
—which are collectively called distortion coefficients. By calibrating the camera and
acquiring the distortion coefficient, the distorted image can be corrected. In this study, the checkerboard
calibration plate was used to remove image distortion. The checkerboard was composed of black and
white grids with regular shapes to easily find the coordinates of corner points and observe the image
correction conveniently. After determining the coordinates of the corners, a transformation matrix was
created to map the distorted points to the undistorted points, and image distortion was removed by
using coordinate transformation. Figure 3 shows the correction of the distorted image, in which the
left and right halves are the original and undistorted images, respectively.
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