Robust Lane Detection and Tracking Prasanth Jeevan Esten Grotli Motivation Autonomous driving Driver assistance (collision avoidance, more precise driving directions)
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Transcript Robust Lane Detection and Tracking Prasanth Jeevan Esten Grotli Motivation Autonomous driving Driver assistance (collision avoidance, more precise driving directions)
Robust Lane Detection and
Tracking
Prasanth Jeevan
Esten Grotli
Motivation
Autonomous driving
Driver assistance (collision avoidance,
more precise driving directions)
Some Terms
Lane detection - draw boundaries of a
lane in a single frame
Lane tracking - uses temporal
coherence to track boundaries in a
frame sequence
Vehicle Orientation- position and
orientation of vehicle within the lane
boundaries
Goals of our lane tracker
Recover lane boundary for straight or
curved lanes in suburban environment
Recover orientation and position of
vehicle in detected lane boundaries
Use temporal coherence for
robustness
Starting with lane detection
Extended the work of Lopez et. al.
2005’s work on lane detection
Ridgel feature
Hyperbola lane model
RANSAC for model fitting
Realtime
Our extension: Temporal coherence for
lane tracking
The Setup
Data: University of Sydney (Berkeley-Sydney Driving Team)
640x480, grayscale, 24 fps
Suburban area of Sydney
Lane Model: Hyperbola
2 lane boundaries
4 parameters
Features: Ridgels
2 for vehicle position and orientation
2 for lane width and curvature
Picks out the center line of lane markers
More robust than simple gradient vectors and edges
Fitting: RANSAC
Robustly fit lane model to ridgel features
Setup
Setup
Setup
The Setup
Data: University of Sydney
640x480, grayscale, 24 fps
Suburban area of Sydney
Lane Model: Hyperbola
2 lane boundaries
4 parameters
Features: Ridgels
2 for vehicle position and orientation
2 for lane width and curvature
Picks out the center line of lane markers
More robust than simple gradient vectors and edges
Fitting: RANSAC
Robustly fit lane model to ridgel features
Lane Model
Assumes flat road,
constant curvature
L and K are the lane
width and road
curvature
and x0 are the
vehicle’s orientation
and position
is the pitch of the
camera, assumed to
be fixed
Lane Model
v is the image row of a lane boundary
uL and uR are the image column of the left
and right lane boundary, respectively
The Setup
Data: University of Sydney (Berkeley-Sydney Driving Team)
640x480, grayscale, 24 fps
Suburban area of Sydney
Lane Model: Hyperbolic
2 lane boundaries
4 parameters
Features: Ridgels
2 for vehicle position and orientation
2 for lane width and curvature
Picks out the center line of lane markers
More robust than simple gradient vectors and edges
Fitting: RANSAC
Robustly fit lane model to ridgel features
Ridgel Feature
Center line of
elongated high
intensity structures
(lane markers)
Originally proposed
for use in rigid
registration of CT
and MRI head
volumes
Ridgel Feature
Recovers dominant
gradient orientation
of pixel
Invariance under
monotonic-grey
level transforms
(shadows) and rigid
movements of
image
The Setup
Data: University of Sydney
640x480, grayscale, 24 fps
Suburban area of Sydney
Lane Model: Hyperbola
2 lane boundaries
4 parameters
Features: Ridgels
2 for vehicle position and orientation
2 for lane width and curvature
Picks out the center line of lane markers
More robust than simple gradient vectors and edges
Fitting: RANSAC
Robustly fit lane model to ridgel features
Fitting with RANSAC
Need a minimum of four ridgels to solve for
L, K, , and x0
Robust to clutter (outliers)
Fitting with RANSAC
Error function
Distance measure
based on # of
pixels between
feature and
boundary
Difference in
orientation of ridgel
and closest lane
boundary point
Temporal Coherence
At 24fps the lane boundaries in
sequential frames are highly correlated
Can remove lots of clutter more
intelligently based on coherence
Doesn’t make sense to use global (whole
image) fixed thresholds for processing a
(slowly) varying scene
Classifying and removing
ridgels
Using the previous lane boundary
Dynamically classify left and right ridgels
per row image gradient comparison
“far left” and “far right” ridgels removed
Velocity Measurements
Optical encoder provides velocity
Model for vehicle motion
Updates lane model parameters and x0
for next frame
Results, original algorithm
QuickTime™ and a
decompressor
are needed to see this picture.
Results, algorithm w/ temporal
QuickTime™ and a
decompressor
are needed to see this picture.
Conclusion
Robust by incorporating temporal features
Still needs work
Theoretical speed up by pruning ridgel
features
Ridgel feature robust
Lane model assumptions may not hold in
non-highway roads
Future Work
Implement in C, possibly using OpenCV
Cluster ridgels together based on location
Possibly work with Berkeley-Sydney Driving
Team to use other sensors to make this
more robust (LIDAR, IMU, etc.)
Acknowledgements
Allen Yang
Dr. Jonathan Sprinkle
University of Sydney
Professor Kosecka
Important works
reviewed/considered
Zhou. et. al. 2006
Particle filter and Tabu Search
Hyperbolic lane model
Sobel edge features
Zu Kim 2006
Particle filtering and RANSAC
Cubic spline lane model
No vehicle orientation/position estimation
Template image matching for features