The knowledge learned during the course will regard the following subjects: Basic and advanced control methods. Filter/observer design. Application of control and filtering to advanced driver assistance systems (ADAS). The skills acquired during the course will be the following: Understanding, analysing and simulating the behaviour of dynamic systems. Developing control and filtering algorithms for dynamic systems and, in particular for ADAS/autonomous driving. Developing simulation and control software in Matlab/Simulink, in particular for ADAS/autonomous driving. Students will learn how to use in a comprehensive way the acquired knowledge and skills in order to deal with new problems, without being limited to a small set of applications/case studies.

Strong background in differential and integral calculus of vector valued functions and in linear algebra. Basic concepts of physics, mechanics, complex numbers. Basic notions on dynamic systems and automatic control. Basic knowledge of Matlab/Simulink programming and simulation. Attended courses: Automatic Control.

The present section reports the topics of the course of Driving Assistance System Design (Part A and B) indicating what are the topics ascribed to Part A and what is the topics ascribed to Part B. This will lead to a better comprehension of the overall organization of the course. 1. Introduction (Part A+Part B - 4.5h) • Description of the Advanced Driver Assistance Systems and the Automated Driving Systems. • Description of the Longitudinal Control Systems. o Cruise Control. o Automated Highway Systems. o Control systems for improving the safety:  Collision Avoidance.  Antilock (ABS) and Antispin (ASR) systems. • Description of the Lateral Control Systems: o Automated Lane Keeping. o Vehicle Dynamics Control. • Description of Suspension Control Systems: o Active Roll Control. o Heave Control (skyhook, groundhook). • The role played by the o Simplified Vehicle Dynamic Models (Rigid Vehicle model, Models describing the Heave motion, Simple Roll Motion). o Basic and Advanced Control Methods. o Complete Vehicle models for the design and simulation of Vehicle Active Control Systems. 2. Recap of systems and control notions (Part A - 7.5h) • General Concept of dynamic systems. • Stability notions. • Linearization. • Laplace transform. • Transfer functions. • Feedback control principle. 3. Longitudinal Dynamic models for Adaptive Cruise Control (Part B 6h) • Recap on driving dynamic performance. • Recap on braking performance. 4. Simplified (non-linear and linearized) Vehicle Dynamic Models for the design of the Automated Lane Keeping and the Simplified Vehicle Dynamic Control (B) (10,5h) Bicycle Model: • Recap on the tire characteristics and models (1,5 h). • Recap on the aerodynamic forces acting on the vehicle (1,5 h). • Computation of the equations of motion in the Configuration Space and in the State Space, analysis of the stability in the small (3 h). • Derivation of the equations of motion at Steady State and study of the effect of the load transfer (contribution of : antiroll bar, longitudinal position of the COG, tire traction and braking force) (4,5 h). 5. Simplified Vehicle Dynamic Models for Heave and Roll Control Design (Part B - 9 h) • Non-linear and linearized dynamic models describing the vertical motion of the vehicle on suspensions (half car model, quarter car model) (6 h). • Simplified single degree of freedom model describing the roll motion (3 h). 6. Control Algorithms: description, design and implementation (Part A - 34.5h) • PID control: o Description of the control architecture and features. o Design of lane keeping and cruise control based on PID controllers using as reference the bicycle model. • Adaptive cruise control, string stability and constant time-gap control: o Description of the control architecture and features o design of the cruise control using as reference the bicycle model. • State feedback control - LQR/LQI control: o Description of the control architecture and features. o Design of the lane keeping using the State feedback control and LQR/LQI control. • Model predictive control (MPC): o Description of the control architecture and features. o Design of the trajectory planning, lateral and longitudinal control based on MPC control using the bicycle model. • Observers/filters, notions about sensor fusion: o Description of State Observers. o Description about sensor fusion. o Implementation of the above mentioned control techniques assuming to measure only some variables and estimating the others. o Implementation of ABS and vehicle stability control using the most adequate control methods described above. 7. Complete vehicle model – 10 dof (Part B - 13,5 h) Description of the equations of motion. • Linearization of the equations of motion and description of the uncoupling between handling and comfort. • Description of the Segel model and study of the handling behaviour of the vehicle. • Description of the comfort model and study of the comfort Longitudinal dynamic behaviour. • Modeling of the torsional dynamic behaviour of the driveline and study of the coupling between the torsional dynamics of the driveline and the longitudinal dynamic behaviour of the vehicle. • Drivability and comfort analysis. 8. Autonomous Vehicle Control - Technologies for Autonomous Vehicle Control (Part A + Part B - 7,5 h) • An overview on the sensor technology (Camera, Radar, Lidar, IMU). • An overview on electronic control unit characteristics for image processing and vehicle control. • An overview on the actuation systems technology for autonomous vehicles. 9. Driver Assistance Vehicle Control Implementation (Part A + Part B - 27 h) Driver Model: • Non-predictive and predictive simplified driver models (integration in the vehicle model). Longitudinal Vehicle Control: • Antilock, Antispin, Cruise Control (Implementation of the Control Strategies using a complete vehicle model – PROJECT 1). Lateral Vehicle Control: • Automated Lane Keeping and Vehicle Dynamic Control (Implementation of the Control Strategies using a complete vehicle model – PROJECT 2).

The course is organized in a sequence of subjects ascribed to Part A and Part B to take the maximum learning comprehension. After an introductory part (Part A and B), teaching will focus on a recap of systems and control notions (Part A). The following part (Part B) will be dedicated to the learning of the simplified vehicle model that are requested to design the controllers for ADAS. Then a wide part will be devoted to the description, design and implementation of the Control Algorithms (Part A). It will follow a part (Part B) dedicated to the learning of the complete vehicle models. Then a part (Part A + Part B) dedicated to the implementation of ADAS controllers on complete vehicle models will conclude the course. Project works in teams will be assigned to this end. The different sections are organized in teaching lectures mainly devoted to the theory part. They will be supported by tutorials dedicated to address design aspects, numerical examples and solved problems on specific issues about vehicle dynamic analysis and vehicle design methodologies. The tutorials will be developed using Matlab/Simulink software. Laboratory exercises will be assigned in the last part of the course and will address the development of the Project Works to be carried out in team. They will be dedicated to implement the Adaptive Cruise Control strategies and the Lane Keeping Assist Controls developed in Part A in a complete vehicle model. A Matlab/Simulink + CARSIM co-simulation approach will be adopted for the scope.

- Lecture material (slides, exercises, Matlab/Simulink files). - G.F. Franklin, J.D. Powell, A. Emami-Naeini, Feedback Control of Dynamic Systems, Prentice Hall, 2009. - Kwakernaak, Huibert & Sivan, Raphael, Linear Optimal Control Systems. Wiley, 1972. - J-J. E. Slotine and W. Li, Applied Nonlinear Control, Prentice Hall, 1991. - F. Borrelli, A. Bemporad, M. Morari, Predictive control for linear and hybrid systems, Cambridge University Press, 2014. - L. Grune and J. Pannek, Nonlinear Model Predictive Control - Theory and Algorithms, Springer, 2011. - G. Genta, Motor Vehicle Dynamics, World Scientific, 2002. - R. Rajamani, Vehicle Dynamics and Control, Springer, 2012. - Preparatory material of automatic control can be found at the website https://didattica.polito.it/laurea_magistrale/mechatronic_engineering/it/presentation

Lecture slides; Exercises; Exercise with solutions ; Lab exercises with solutions; Video lectures (previous years); Simulation tools; Student collaboration tools;

Exam: Computer lab-based test; Optional oral exam; Group project;

The assessment is devoted to verify the comprehension of the topics that have been covered throughout the whole course of Driver Assistance System Design (Part A and B together). The assessment is also addressed to verify the ability of the student to use adequate design methodologies and mathematical tools for the design of the controllers for ADAS and the consequent verification of the vehicle performance. Additionally, the presentation and discussion of the project reports is intended to verify the ability of the students to work in team on the application of a real case by using numerical modelling tools adopted in the field of automotive industry. WRITTEN EXAM • Written Exam part is composed by 4 open questions and 10 multiple choice. • Total duration: 2:30 h. • Part A: 5 Multiple Choice questions + 2 open questions. • Part B: 5 Multiple Choice questions + 2 open questions. • Part A and Part B questions will appear in a single assignment. • The grade to be assigned to the Multiple Choice questions rather than the Open Questions can change depending on the level of difficulty of a single question. • A negative score is assigned to wrong answers (Multiple Choice questions). • Both Open Questions and Multiple Choice Questions may be in the form of exercises or design problems. • The grade is rated out of thirty. • The admission to the oral and/or project discussion is possible only if the grade of the written part is ≥ 18/30. • No material is allowed, except the one provided during the exam by the teachers. • Allowed software: Matlab/Simulink, pdf reader. Any other software is forbidden. • Navigation is forbidden. • Taking photos and screenshots is forbidden. • White paper sheets for handwritten calculations are allowed. A small number of separated sheets should be used. Paper notebooks of any kind are not allowed. • The validity of the score is limited to the exam session. ORAL EXAM • The Oral Exam is optional, depending on the grading of the written part (if the score of the written part is in between 18 and 25 included). • Project report presentation and discussion is mandatory. +-3 points will be assigned after the discussion.

In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.