How it works? Lane detection
What’s the first step to make a fully autonomous vehicle? No doubt that it is line detection. It has made some great steps forward in past few years but competition still remains open. We are facing some great challenges like shadows, varying light condition and worn out lane lines.
Lane line detection is divided into these 4 key steps. First of all, lane line pixels are pooled with a ridge detector. After that effective no is filtering mechanism goes trough and remove them to a large extent. Modified version of sequential script ensure that each lane line is captured correctly. In the end if the lane lines exist on each side of the road a parallelism reinforcement technique is imposed to improve the model accuracy. This kind of model is able to generate lane lines with high success rate. It is also possible to generate precise and consistent vehicle localization with respect of road lane lines.
Most of the lane line detection systems start with extraction of different images features such as edges and color changes. Through machine learning methods, vector machine and boost classification the road lane lines are getting build. Model is getting nice straight lines or parametric curves. Some of algorithms may also include third party check of built lane line such as Kalman or particle filter.
The noise filtering mechanisms is not robust and effective. It is mainly done though thresholding gradient magnitude. Nevertheless, some of higher gradient values are there due to shadows and object surrounding them and lower gradient values are caused by poor lighting condition or worn out road lane lines. The neural networks should help with detection. It can build a training set that has enough samples under variety of scenarios. If the common situation in which one the algorithm failed. It will result that the same mistake will not be done any time in the future.
To conclude we described key algorithms for lane line detection. These algorithms should provide a reliance vision-based road line detection which should work even in challenging road situation thanks to rigidness and noise cancellation. To improve these algorithms, we can add the particle filter. This will make the image even more consistent and smooth. Some of the similar algorithms can bring us stereo vision to get the depth of information. The road curvatures estimation will be fused from map and GPS module in our car to improve even better consistency of the road lane line detection.
