LaNet - Unidirectional LSTM architecture for lane identification using Accelerometer data

Reference Codebase for LaNet proposal submitted to ACM BuildSys 2019.

Data for model training available at : https://drive.google.com/file/d/1keAI9dy1iqarq84IJfWamleNDFrEMXkd/view?usp=sharing

• Codes for LaNet LSTM arch along with WeightedLSTMCrossEntropyLoss are included.
• Supports Tensorboard visualisations to visualize incremental accuracy observed in each LSTM Cell
• Provides scope for reconfiguration of model parameters including sub-drive length and sub-segment lengths.

• Sample Length "l" = 800000
• Sub-drive stride "s" = 50000
• Sub-segment length"d" = 50000
• Sub-segment stride "m" = int(50000/2)
• LSTM Hidden dimension "H" = 300
• Learning rate "lr" = 0.005
• Num epochs = 4
• Number of LSTM Layers = 2
• Number of lanes/num_classes = 2
• Accelerometer data "sampling_rate" = 2000
• Batch Size = 512