A modulized lane-follower for driverless vehicles using multi-frame

Abstract

As a fundamental function, lane following plays an important role for driverless vehicles. Unfortunately, lane followers generally confront great difficulty in lane line missed situations caused by vague line, shadows etc. However, for most lane line missed situation, clues of the line may be hidden in prior view of it. Consequently, a lane follower called UNL Lane Follower, which contains two deep learning network modules is proposed. The first module is a lane line detection model called UNET_CLB. Here, the sequence of image frames is utilised rather than only the current frame to deal with the missing lane lines. The second module is a lane-following model called LSTM_DTS, which combines a deep learning attention mechanism (temporal attention network and spatial attention network) with a recurrent neural network. As a result, the proposed UNL Lane Follower produces smoother driving behaviour, especially when a lane line is temporally missed. For better explain ability, the role of each part of the network structure is analysed and explained intuitively. As a modularised network, the UNET_CLB is firstly trained and tested on the TuSimple dataset and CULane dataset. The LSTM_DTS lane follow is then trained and tested on our actual lane following dataset. Finally, the UNL Lane Follower is trained and tested as a whole in a simulation running on Webots, after importing the weight of the two modules trained separately. All testing results showed that the UNL Lane Follower can provide better robustness and accuracy for lane line following mission in the line missed situations.