  The resolution of GPS measurements, especially in urban areas, is
insufficient for identifying a vehicle's lane. In this work, we develop a deep
LSTM neural network model LaNet that determines the lane vehicles are on by
periodically classifying accelerometer samples collected by vehicles as they
drive in real time. Our key finding is that even adjacent patches of road
surfaces contain characteristics that are sufficiently unique to differentiate
between lanes, i.e., roads inherently exhibit differing bumps, cracks,
potholes, and surface unevenness. Cars can capture this road surface
information as they drive using inexpensive, easy-to-install accelerometers
that increasingly come fitted in cars and can be accessed via the CAN-bus. We
collect an aggregate of 60 km driving data and synthesize more based on this
that capture factors such as variable driving speed, vehicle suspensions, and
accelerometer noise. Our formulated LSTM-based deep learning model, LaNet,
learns lane-specific sequences of road surface events (bumps, cracks etc.) and
yields 100% lane classification accuracy with 200 meters of driving data,
achieving over 90% with just 100 m (correspondingly to roughly one minute of
driving). We design the LaNet model to be practical for use in real-time lane
classification and show with extensive experiments that LaNet yields high
classification accuracy even on smooth roads, on large multi-lane roads, and on
drives with frequent lane changes. Since different road surfaces have different
inherent characteristics or entropy, we excavate our neural network model and
discover a mechanism to easily characterize the achievable classification
accuracies in a road over various driving distances by training the model just
once. We present LaNet as a low-cost, easily deployable and highly accurate way
to achieve fine-grained lane identification.
