Course Contents
[list]
[*]History of Automated Driving
[*]Terminology and Paths towards Automated Driving
[*]Architectures, Building Blocks, and Components
[*]Perception & Environment Models
[*]Data Fusion & State Estimation
[list]
[*]Deep Dive: Target Tracking & Traffic Participant Fusion
[*]Deep Dive: Grid Fusion & Free Space Estimation
[*]Deep Dive: Road Model Fusion
[/list]
[*]Localization, Digital Maps, and Vehicle-To-X Communication
[*]Situation Understanding, Prediction, and Criticality Assessment
[list]
[*]Deep Dive: Probabilistic Driving Maneuver Detection
[/list]
[*]Behavior & Trajectory Planning, Decision Making
[*]Automated Driving Software Development & Test
[*]Open Challenges & State-of-the-Art Research Topics
[/list]
Literature
Own lecture slides are distributed in advance of any lecture. For more detailed insights into the topic area, the following books can be recommended:
[list]
[*]Eskandarian, A.: Handbook of Intelligent Vehicles. Springer, London, 2012.
[*]Siciliano, B.; Khatib, O.: Springer Handbook of Robotics. 2[sup]nd[/sup] Edition, Springer, Berlin Heidelberg 2016.
[*]Thrun, S.; Burgard, W.; Fox, D.: Probabilistic Robotics. Intelligent Robotics and Autonomous Agents. The MIT Press, Cambridge, 2006.
[*]Watzenig, D.; Horn, M.: Automated Driving. Safer and More Efficient Future Driving. Springer, Switzerland, 2017.
[*]Winner, H. et al.: Handbook of Driver Assistance Systems. Basic Information, Components and Systems for Active Safety and Comfort. Springer, Switzerland, 2016.
[/list]
Preconditions
Basic knowledge of Linear Algebra, Probability Theory and State Space Systems
[list]
[*]History of Automated Driving
[*]Terminology and Paths towards Automated Driving
[*]Architectures, Building Blocks, and Components
[*]Perception & Environment Models
[*]Data Fusion & State Estimation
[list]
[*]Deep Dive: Target Tracking & Traffic Participant Fusion
[*]Deep Dive: Grid Fusion & Free Space Estimation
[*]Deep Dive: Road Model Fusion
[/list]
[*]Localization, Digital Maps, and Vehicle-To-X Communication
[*]Situation Understanding, Prediction, and Criticality Assessment
[list]
[*]Deep Dive: Probabilistic Driving Maneuver Detection
[/list]
[*]Behavior & Trajectory Planning, Decision Making
[*]Automated Driving Software Development & Test
[*]Open Challenges & State-of-the-Art Research Topics
[/list]
Literature
Own lecture slides are distributed in advance of any lecture. For more detailed insights into the topic area, the following books can be recommended:
[list]
[*]Eskandarian, A.: Handbook of Intelligent Vehicles. Springer, London, 2012.
[*]Siciliano, B.; Khatib, O.: Springer Handbook of Robotics. 2[sup]nd[/sup] Edition, Springer, Berlin Heidelberg 2016.
[*]Thrun, S.; Burgard, W.; Fox, D.: Probabilistic Robotics. Intelligent Robotics and Autonomous Agents. The MIT Press, Cambridge, 2006.
[*]Watzenig, D.; Horn, M.: Automated Driving. Safer and More Efficient Future Driving. Springer, Switzerland, 2017.
[*]Winner, H. et al.: Handbook of Driver Assistance Systems. Basic Information, Components and Systems for Active Safety and Comfort. Springer, Switzerland, 2016.
[/list]
Preconditions
Basic knowledge of Linear Algebra, Probability Theory and State Space Systems
- Lehrende: Matthias Schreier
Semester: Inverno 2023/24