LaNet: Real-time Lane Identification by Learning Road SurfaceCharacteristics from Accelerometer Data

04/06/2020
by   Madhumitha Harishankar, et al.
0

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 achieving over 90 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.

READ FULL TEXT

page 7

page 9

page 13

page 19

page 20

research
08/07/2022

Emerging cooperation on the road by myopic local interactions

We study a combinatorial problem inspired by the following scenario: ful...
research
07/04/2018

LaneNet: Real-Time Lane Detection Networks for Autonomous Driving

Lane detection is to detect lanes on the road and provide the accurate l...
research
06/15/2018

Ego-Lane Analysis System (ELAS): Dataset and Algorithms

Decreasing costs of vision sensors and advances in embedded hardware boo...
research
02/16/2019

A Fleet of Miniature Cars for Experiments in Cooperative Driving

We introduce a unique experimental testbed that consists of a fleet of 1...
research
10/11/2022

Automatic Real-time Vehicle Classification by Image Colour Component Based Template Matching

Selection of appropriate template matching algorithms to run effectively...
research
10/29/2020

Identifying safe intersection design through unsupervised feature extraction from satellite imagery

The World Health Organization has listed the design of safer intersectio...
research
08/07/2023

System Identification and Control of Front-Steered Ackermann Vehicles through Differentiable Physics

In this paper, we address the problem of system identification and contr...

Please sign up or login with your details

Forgot password? Click here to reset