AVL List GmbH (Headquarters) Autonomous Driving. Validation and Testing - Challenges. Dr. Mihai Nica, Hermann Felbinger. Public

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1 AVL List GmbH (Headquarters) Autonomous Driving Validation and Testing - Challenges Dr. Mihai Nica, Hermann Felbinger

2 Our Experience for your Success AVL achieves unique results in regards to the development and improvement of all types of powertrains as well as in the field of measurement and test technology. AVL over 70 years experience Involved in more than 1,500 engine development projects More than 4,000 engine testbed installations Dr. Mihai Nica, Hermann Felbinger 22 Oktober

3 Enterprise Development Automotive RESEARCH 10% of turnover in-house R&D INNOVATION 1500 granted patents STAFF employees 65% engineers and scientists GLOBAL FOOTPRINT 30 engineering locations >220 testbeds Global customer support network GROWTH SALES 1995: 0.15 billion 2017: 1.55 billion Plan 2018: 1.71 billion EXPERIENCE 70 years! 5 powertrain elements ONE PARTNER Dr. Mihai Nica, Hermann Felbinger 22 Oktober

4 Five elements of the powertrain Dr. Mihai Nica, Hermann Felbinger 22 Oktober

5 Autonomous Driving Benefits, Roadmap, System Dr. Mihai Nica, Hermann Felbinger 22 Oktober

6 Benefits of Advanced Driving Assistance Systems Dr. Mihai Nica, Hermann Felbinger 22 Oktober

7 AVL System Design Use case and test case definitions for high level features (L3+) HARA hazard & risk analysis Controller architecture, sensor layout Benchmarking & targets setting Control & SW Development Proof & realize customer specific new ADAS & AD features Adapt, modify features e.g. regional differences Adv. Predictive Functions Smart & connected energy management function development Predictive controls design & calibration improving vehicle attributes Calibration, Testing& Validation Features carry-over integration to derivatives Time reduced validation & scenario generation L3+ functions Perceived safety & comfort quality optimization & assessment Engineering solutions for series (L0-L3) & adv. development (L3+), applicable at PC & CV Dr. Mihai Nica, Hermann Felbinger 22 Oktober

8 LEVEL 0,1 LEVEL 2 LEVEL 3 LEVEL4 LEVEL 5 Roadmap for Autonomous Driving accident free driving Full automation Full automation Conditional Automation Partial Automation Park Assist Highway Assist Auto Parking Traffic Jam Assist Highway driving Lane Change Control Automated City Driving Automated Rural Roads Automated Highway City Pilot Rural Roads Pilot Highway Pilot AEB - Autonomous Emergency Braking LKA - Lane Keep Assist ACC Adaptive Cruise Control *only main functions shown Dr. Mihai Nica, Hermann Felbinger 22 Oktober

9 ENVIRONMENT ADAS/AD Systems Sensors Assessment Decision Making Actuators Camera Localization Path Planning Steering unit Radar Brakes Ultrasonic Lane/Road Identification Collision Avoidance Throttle Maps/ Navigation Object Classification Behaviour Monitor HMI Dr. Mihai Nica, Hermann Felbinger 22 Oktober

10 Combination of Sensors Dr. Mihai Nica, Hermann Felbinger 22 Oktober

11 Sensor-fusion Good for recognising objects Measuring lateral movement Sensor Fusion Good Measuring range Measuring relative velocity Relative velocity of the object in the front Position and classification of the object Longitudinal and lateral distance of the object Dr. Mihai Nica, Hermann Felbinger 22 Oktober

12 Validation and Test One of the issues encountered by engineers looking to sign off advanced driver assistance systems (ADAS) is how to test in a realistic environment. Test tracks provide the most controlled and repeatable conditions but real road driving is more indicative of how the systems will operate in normal use. Issues with the current method of Testing/Validation With increasing number of sensors and complexity of ADAS features, the number of operating conditions for a feature are exponentially increasing. Real road testing is not a feasible and efficient methodology to test various conditions and complexity. Most of the testing validation activities are conducted subjectively. No proper way of verifying objective functionality of the feature. Today, to test the camera-based ADAS, test vehicles are equipped with these systems and are performing long hours of driving that can last for years. These tests are used to validate the use of the function and to verify its response to the requirement. The identification of all potential failure modes and their interactions is a very time-consuming process. It is difficult to reproduce the test conditions and failure modes under which the control system operates Dr. Mihai Nica, Hermann Felbinger 22 Oktober

13 Validation and Test AVL Dr. Mihai Nica, Hermann Felbinger 22 Oktober

14 Strong ADAS/AD Testing Capabilities From Road to XiL & Lab Autonomous Driving & Vehicle Development Center with Demo Cars Test Region Styria public road validation Test track NCAP & features evaluation Fleet testing online OTA assessment Driver in the loop safe & reproducible Vehicle in the loop DrivingCube Virtual Validation with simulation & cloud Comprehensive test environment for basic and advanced ADAS + AD features in lab, on XiL and road Dr. Mihai Nica, Hermann Felbinger 22 Oktober

15 ADAS/AD Test Environments Software testing: Model in the loop (office, cloud) Performance tests: Tools for Tests on public roads Usability testing: Driving simulator Safety tests Tools for Tests on private proving ground ADAS/AD system qualification Vehicle in the loop Dr. Mihai Nica, Hermann Felbinger 22 Oktober

16 ADAS Testing Tool Integration Dr. Mihai Nica, Hermann Felbinger 22 Oktober

17 ADAS Testing Tool Content Radar Stimulus Camera Stimulus Ultrasonic Stimulus Radar Simulation Camera Simulation RT Sim. Platform Etc. Parameters Input Model Motion Error Maps Motion Error Imprint Tracking Standard deviation Object List Object List Filter Matrix Smoohting Model Object Fusion/Detection Standard deviation RDI Noise Level Noise Map Noise Imprint Standard deviation Antenna Pattern Antenna Model Object List Antenna gain characteristic Atmospheric attenuation Attenuation Map Environment Model RDI Input Interface Fog, Rain and Snow attenuation Resolution Ray Tracing RDI Model UDP Interface Ideal Sensor Ray depth Shared Memory Object List Dr. Mihai Nica, Hermann Felbinger 22 Oktober

18 Automated Assessment of Driver Perception How does she feel? AVL-DRIVE ADAS Scale Safety & Comfort 9-10 very convenient 8-9 convenient 7-8 acceptable 6-7 almost acceptable 5-6 disturbing 4-5 very disturbing 3-4 almost unacceptable 2-3 unacceptable 1-2 fail sec AVL-DRIVE TM AUTONOMOUS DRIVING MOVIE Lane Assist Lane Lateral Dist. Lane Precision Ctrl. Left Quality Neuronal Dist. Lateral Lane Lane Precision Ctrl. Left Quality Neuronal Lateral Dist. Lane Lane Precision Usage Control Ctrl. Right Left Quality Quality AVL USP: Objective measurement technology for perceived safety & comfort feeling w Dr. Mihai Nica, Hermann Felbinger 22 Oktober

19 Thank You Contact: