CNS/EE 286 Section III
Dynamic Machine Vision for Intelligent Vehicles
Course Syllabus
- Week 1: Introduction General biological vs. technical
bases. What is meant by "intelligent"? Types of intelligent vehicles:
autonomous vs. assistant systems. Modeling of relevant aspects of the
world; visual environment on Earth: lighting conditions, dependence on
time and location. Structures, objects, buildings, vehicles and living
beings.
- Week 2: Processes Differential and integral
representations; specific properties for spatial and temporal
domains. Constraints to temporal changes: differential equations,
state and control variables, parameters. Multiple scales and local
integrals. Vehicle dynamics: longitudinal and lateral degrees of
freedom, controls. Sensors and actuators as interfaces to the real
world.
- Week 3: The 4-D approach do dynamic machine
perception Recursive estimation techniques, extension to perspective
imaging, orientation towards generic classes of physical objects and
expectations. Intelligent control of image processing steps; data
fusion through dynamical and measurement models. Homogeneous
coordinates, translations, rotations, perspective mapping.
- Week 4: Image feature extraction Area- (region-)
vs. edge based features, gradients and their extremal values, the
CRONOS software package geared to the 4-D approach. Area-based
features, intensities, colors, textures, multiple scales and image
pyramids, the TRIANGLE algorithm for real-time dynamic scene
understanding. Intelligent control for feature extraction in the 4-D
approach.
- Week 5: Roads and relative vehicle states Planar road
models: averaged moving model, locally fixed models, extensions to 3-D
road models. Vehicle state and road parameter estimation (road
perception), imaging geometry, range and multifocal vision, recursive
estimation.
- Week 6: Obstacle recognition and relative state
estimation Systematic search for feature detection, controlled
additional search for hypothesis generation. Multi-object recognition
and tracking: object detection, hypothesis testing, state estimation.
Shape recognition.
- Week 7: Vehicle control and behavioral capabilities
Longitudinal degrees of freedom, throttle and brakes, lateral control:
steering angle and trajectory curvature. Feedback-based behavioral
capabilities, feed-forward control elements, superposition for robust
maneuver elements. Symbolic representations of capabilities and
maneuver elements.
- Week 8: Mission performance. Sequencing of behavioral
capabilities Mission decomposition (planning), monitoring,
special task domains. General aspects of system integration; examples:
VaMP, functionalities required for driving on freeways; VaMoRs,
driving on minor road nets. System structure, implementational
aspects, experimental results.
- Week 9: Summary and outlook Autonomous performance
vs. driver assistance (haptic warnings, but no override) vs. survival
functions (override for safety reasons only); development trends. What
are the limits?
soatto@vision.caltech.edu
California Institute of Technology,
Pasadena, CA 91125.