Future Vehicle Technology

Future Vehicle Technology © VIRTUAL VEHICLE

ESR 1: Integrated design and simulation for active safety functions M. Sc. (Univ.) Michael Hartmann VIRTUAL VEHICLE Research Center

VIRTUAL VEHICLE Research Center is funded within the COMET – Competence Centers for Excellent Technologies – programme by the Austrian Federal Ministry for Transport, Innovation and Technology (BMVIT), the Federal Ministry of Science, Research and Economy (BMWFW), the Austrian Research Promotion Agency (FFG), the province of Styria and the Styrian Business Promotion Agency (SFG). The COMET programme is administrated by FFG.

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Short Biography

2006 - 2010 •

Diploma (FH) in Electrical Engineering; University of Applied Sciences Würzburg-Schweinfurt

2010 - 2013 • • •

Master of Science (M. Sc.) in Electrical Engineering and Information Technology; Technical University Munich Master Thesis: Probabilistic Movement Prediction in Soccer Student Research Assistant in Intelligent Autonomous Systems Lab (CoTeSys)

2013 – 2015 •

Bertrandt GmbH Munich, Virtual Validation and Software Testing for BMW Group

Research Interests: Motion Planning, Probabilistic Machine Learning, Movement Prediction and Optimal Control July 2016 / Hartmann

Integrated design and simulation for active safety functions

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Vision

Source: Volvo

Control in dynamic and uncertain environments Source of uncertainty: predictability of the nearby vehicles and pedestrians (future trajectories) July 2016 / Hartmann


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Psychological aspects in driving situations

July 2016 / Hartmann


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Environment Sensing Situation Recognition Movement Prediction Source: University of Central Florida

Situation Rating Path Planning

Tracking of the… -

… nearby vehicles and pedestrians

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… form of obstacles

Optimal Control July 2016 / Hartmann


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Environment Sensing Situation Recognition Movement Prediction


Source: Kitani, Carnegie Mellon University

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Semantic labeling of the environment

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Machine Learning for situation assessment

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Complexity (e.g. amount of situations)



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Source: Lefevre, University of California at Berkeley

Environment Sensing Situation Recognition Physics-based motion models:

Movement Prediction

Situation Rating

- Represent vehicles as dynamic entities governed by the laws of physics - Future motion is predicted using dynamic and kinematic models Limitations:

Path Planning


- Limited to short-term prediction - Unable to anticipate any change in the motion of the car caused by  the execution of a maneuver  external factors (e.g. front vehicle)


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Situation Rating


Maneuver-based motion models: - Vehicles follow typical patterns (long-term intention) Limitations:

Path Planning


- Heavy computational burden - not incorporating physical limitations of a vehicle


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Interaction-aware motion models: - Vehicles which interact with each other

Situation Rating

- Leads to a better interpretation (known dependencies) Limitations:

Path Planning

- Computationally expensive - Not compatible with real-time risk assessment



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Situation Rating for Safety Environment Sensing Situation Recognition Movement Prediction


Source: Streubel; TU Chemnitz

1. Risk of a (future) situation a) Binary Collision prediction b) Probabilistic Collision prediction 2. Uncertainty

3. Driving task demand a) Speed b) Road Geometry c) Unexpected behavior d) Other road users



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Environment Sensing

Based on the current state x(t):

Situation Recognition

• Choose optimal action sequence based on the situation rating (e.g. minimal collision risk) x(t)

Movement Prediction


Optimal Control Source: Maler, O.; CNRS Verimag, France



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Model Predictive Control Environment Sensing Situation Recognition Movement Prediction


Optimal Control Source: Lecture Slides Prof. Bemporad (http://cse.lab.imtlucca.it/) July 2016 / Hartmann


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Challenge

- Movement prediction of pedestrians and vehicles - Reasoning about the situations (e.g. Causal Inference) - Motion planning for safe autonomous navigation in uncertain environments in combination with optimal control - Dynamic obstacle avoidance in real-time



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Recommended Literature

Lefèvre, S., Vasquez, D., & Laugier, C. (2014). A survey on motion prediction and risk assessment for intelligent vehicles. Robomech Journal, 1(1), 1.

Ziebart, B. D. (2010). Modeling purposeful adaptive behavior with the principle of maximum causal entropy. (Doctoral dissertation, Carnegie Mellon University. 2010)

Aoude, G. S. (2011). Threat assessment for safe navigation in environments with uncertainty in predictability (Doctoral dissertation, Massachusetts Institute of Technology).



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M.Sc. (Univ.) Michael Hartmann VIRTUAL VEHICLE Research Center [email protected]

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MI1 "Integrated design and simulation for active safety functions"

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Advanced control approaches in driver assistance and active safety systems for MAGV incl. relevant sensor and actuator systems.

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Active safety controllers will be based on specified computational intelligence methods

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Improvements in vehicle safety and performance according to the formulated assessment criteria

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Validation of the controllers will be carried out on specially developed realtime co-simulation platform, where specified MAGV subsystems and ECU (as components of hardware-in-the-loop (HIL) experiment)

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Controller models are integrated into one co-simulation environment.

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New co-simulation platform will be also proposed for verification and validation tasks of other individual ESR projects.



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