Lane Detection and Classification using Cascaded CNNs

2 Jul 2019  ·  Fabio Pizzati, Marco Allodi, Alejandro Barrera, Fernando García ·

Lane detection is extremely important for autonomous vehicles. For this reason, many approaches use lane boundary information to locate the vehicle inside the street, or to integrate GPS-based localization. As many other computer vision based tasks, convolutional neural networks (CNNs) represent the state-of-the-art technology to indentify lane boundaries. However, the position of the lane boundaries w.r.t. the vehicle may not suffice for a reliable positioning, as for path planning or localization information regarding lane types may also be needed. In this work, we present an end-to-end system for lane boundary identification, clustering and classification, based on two cascaded neural networks, that runs in real-time. To build the system, 14336 lane boundaries instances of the TuSimple dataset for lane detection have been labelled using 8 different classes. Our dataset and the code for inference are available online.

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Introduced in the Paper:

TuSimple Lane

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Lane Detection TuSimple End-to-end ERFNet Accuracy 95.24% # 30
F1 score 90.82 # 25


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