MTech Driverless Vehicles syllabus for 2 Sem 2020 scheme 20MAU241

Module-1 Introduction to autonomous driving 0 hours

Introduction to autonomous driving:

autonomous driving technologies overview, autonomous driving algorithms: Sensing, Perception, Object Recognition and Tracking:

 

Autonomous driving client system: Robot Operating System, Hardware platform:

Autonomous driving cloud platform: Simulation, HD Map Production, Deep learning Model Training

Module-2 Autonomous vehicle localization 0 hours

Autonomous vehicle localization:

Localization with GNSS: GNSS overview, GNSS error analysis, satellite based augmentation systems, real time kinematic and differential GPS, precise point positioning, GNSS INS integration Localization with LiDAR and HD maps

 

Visual Odometry:

Stereo Visual Odometry, Monocular Visual Odometry, Visual Inertial Odometry, Dead Reckoning and Wheel Odometry; Sensor fusion

A d v e r t i s e m e n t
Module-3 Perceptions In Autonomous driving 0 hours

Perceptions In Autonomous driving:

Introduction, Datasets, Detection, Segmentation, Sterio, Optical flow and Scene flow

 

Deep learning in Autonomous Driving Perception:

Convolutional Neural Networks, Detection, Semantic segmentation, Stereo and optical flow

Module-4 Prediction and Routing 0 hours

Prediction and Routing:

Planning and control overview, Traffic prediction: Behaviour prediction as classification, Vehicle trajectory generation,

 

Lane level routing:

Constructing a weighted directed graph for routing, typical routing algorithms, routing graph cost

Module-5 Decision planning and control 0 hours

Decision planning and control:

Behavioural decisions, Motion planning, Feedback control Reinforcement Learning Based Planning and Control,

 

Client systems for Autonomous Driving:

Operating systems and computing platform

 

Cloud platform for Autonomous driving:

Introduction, infrastructure , simulation

 

Course outcomes:

At the end of the course the student will be able to:

CO1:Understand the Autonomous system’s and its requirements

CO2:Explain algorithm, sensing, object recognition and tracking of an Autonomous system

CO3:Do the error analysis of Localization systems and use the tools and techniques

CO4:Explain, plan and control the traffic behaviour, and shall be able to do lane level routing and create simple algorithms

CO5:Explain Plan and control motion, choose proper client systems for automotive vehicles and understand the cloud platform

 

Question paper pattern:

The SEE question paper will be set for 100 marks and the marks scored will be proportionately reduced to 60.

  • The question paper will have ten full questions carrying equal marks.
  • Each full question is for 20 marks.
  • There will be two full questions (with a maximum of four sub questions) from each module.
  • Each full question will have sub question covering all the topics under a module.
  • The students will have to answer five full questions, selecting one full question from each module.

 

Textbook/ Textbooks

(1) Shaoshan Liu, Liyun Li, Jie Tang, Shuang Wu, Jean-Luc, Creating Autonomous Vehicle Systems Morgan & Claypool Publishers 1st Edition, 2018

(2) Ronald K. Jurgen Autonomous Vehicles for Safer Driving SAE International Edition , 2013

 

Reference Books

(1) Hod Lipson, Melba Kurman Driverless: Intelligent Cars and the Road ahead MIT Press. 1st Edition, 2016

(2) Markus Maurer, J. Christian Gerdes, Barbara Lenz Autonomous Driving: Technical, Legal and Social Aspects 1st Edition, 2016

(3) Hannah YeeFen Lim, Autonomous Vehicles and the Law: Technology, Algorithms and Ethics ,Edward Elgar Publishing. 1st Edition, 2018

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