Syllabus
Lab Content 45 hours
- Utilizing a monocular camera for object
- Employing LIDAR for object
- Utilizing RADAR for object
- Object tracking via
- Object tracking employing stereo camera
- Measuring object distances using
- Utilizing RADAR for object distance
- Employing Stereo camera technology for object distance
- Camera-based lane
- Optical character recognition applied for detecting traffic signs
Unit 1
Introduction to ADAS: Introduction – Terminology, Design consideration, Safety assessment. Commonly used hardware, main components of software stack, Vehicle modelling and control, safety frameworks and current industry practices. State Estimation and Localization – Least squares – Vehicle localization sensors – GPS and IMU – Extended Kalman filter, unscented Kalman filter – LIDAR scan matching, iterative Closest Point Algorithm – Multiple sensor fusion for vehicle state estimation and localization.
Unit 2
Feedforward neural networks: – Review of Deep Learning, Multilayer Perceptron, Optimization, Stochastic Gradient Descent, Back propagation – Review of Convolutional Neural Networks (CNN): Architecture, Convolution/Pooling layers – Understanding and Visualizing CNN. Visual Perception – Visual Perception – Pinhole camera model, intrinsic and extrinsic camera calibration, monocular and stereo vision, projective geometry – CNNs for 2 D Object detection, Semantic segmentation.
Unit 3
Motion Planning: – Driving Missions, Scenarios, and Behaviour, Motion Planning Constraints, Objective Functions for Autonomous Driving, Hierarchical Motion Planning – Occupancy Grids, Populating Occupancy Grids from LIDAR Scan Data, Occupancy Grid Updates, High Definition Road Maps. ACC, AEBs, LDWS, LKA Creating a Road Network Graph, Dijkstra’s Shortest Path Search, A* Shortest Path Search, Motion Prediction, Map-Aware Motion Prediction, Time to Collision. UNECE, GSR, Indian regulations.
Objectives and Outcomes
Course Objectives
- To provide knowledge on fundamental concepts, terminologies, and design considerations in autonomous vehicle systems.
- To make students’ understand state estimation and localization techniques including least squares, Kalman filters, and sensor fusion for accurate positioning of autonomous vehicles.
- To familiarize students with neural networks and deep learning techniques, focusing on feedforward neural networks and convolutional neural networks (CNNs)
- To familiarize in motion planning algorithms and techniques, including driving missions, occupancy grids, and path planning algorithms
Course Outcomes
CO
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CO Description
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CO1
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Analyse and evaluate the safety considerations and design aspects in autonomous vehicle systems.
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CO2
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Implement state estimation and localization algorithms using sensor data, demonstrating proficiency
in techniques such as Kalman filtering and sensor fusion for accurate vehicle positioning.
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CO3
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Designing and training neural networks for various tasks in autonomous driving, including object
detection and segmentation, utilizing deep learning frameworks.
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CO4
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Develop and implement motion planning algorithms and techniques, including path planning and
obstacle avoidance through simulation.
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CO-PO Mapping
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PO1
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PO2
|
PO3
|
PO4
|
PO5
|
CO1
|
3
|
|
1
|
1
|
3
|
CO2
|
2
|
2
|
|
1
|
3
|
CO3
|
2
|
2
|
1
|
3
|
3
|
CO4
|
3
|
2
|
1
|
3
|
3
|
Skills acquired
Designing and implementing state estimation algorithms, neural networks for perception tasks, motion planning algorithms, and utilizing sensor data for object detection and tracking in autonomous vehicle systems.