Investigation of Developing Vehicle Technologies

Transcription

1 Investigation of Developing Vehicle Technologies Mark Fabbroni B.A.Sc., M.A.Sc., P.Eng. Practice Lead Collision Reconstruction & Trucking 30 Forensic Engineering September 17, 2018

2 Agenda How does new tech spread in a vehicle population? Where are we now in terms of autonomous tech? How are defects discovered? Given the above, what s our investigative reality? Case Study

3 Electronic Stability Control Standard on all light duty vehicles in the U.S. since September 1, 2011 IIHS HLDI 2015

4 Electronic Stability Control Standard on all light duty vehicles in the U.S. since September 1, 2011 IIHS HLDI 2015

5 Where are we now? NHTSA 2018 IIHS Twitter 2018

6 Where are we going? IIHS HLDI 2015

7 Recalls in Canada

8 Famous Recalls Toyota Sudden Accel, Model Years 2004 to Year Gap! GM Ignition Switches, 2014 Model Years 2005 to Year Gap! Takata Airbags, OEMs, Model Years 2002 to Year Gap!

9 Summary Decades away from wide-spread coverage of Level 1 & 2 automation systems Far more from wide-spread Level 5 automation The vehicle population will contain a mix of automation levels at various levels of development for decades to come In any given case, could be discovering a defect leading to a future recall

10 How to Handle? Preservation of evidence Immediate engineering involvement Evidence examination, testing, etc. Immediate lawyer involvement Involvement of manufacturer, disclosure of proprietary info, etc. Need the Right Case Costly & lengthy litigation

11 Case Study Uber Self-Driving Vehicle

12 Case Study Uber Self-Driving Vehicle Uber equipped the vehicle with a developmental, self-driving system: Forward- and side-facing cameras Radar Lidar Navigation sensors Vehicle also equipped by Volvo with several advanced driver assistance functions: Collision avoidance with automatic emergency braking Functions for detecting driver alertness The Volvo functions were disabled when the test vehicle was operated in computer control mode

13 Case Study Uber Self-Driving Vehicle Dark clothing Did not look in the direction of the vehicle until just before impact Crossed road in a section not directly illuminated by lighting Pedestrian entered the roadway from a brick median, where signs facing toward the roadway warn pedestrians to use a crosswalk, which is located 360 feet north of the Mill Avenue crash site. Bicycle did not have side reflectors Pedestrian s post-accident toxicology test results positive for methamphetamine and marijuana.

14 Case Study Uber Self-Driving Vehicle The system first registered radar and LIDAR observations of the pedestrian about six seconds before impact, when the vehicle was traveling 43 mph. The self-driving system software classified the pedestrian as an unknown object, as a vehicle, and then as a bicycle with varying expectations of future travel path. At 1.3 seconds before impact, the self-driving system determined that emergency braking was needed to mitigate a collision. Emergency braking maneuvers were not enabled while the vehicle was under computer control to reduce the potential for erratic vehicle behavior. The vehicle operator was relied on to intervene and take action, BUT the system is not designed to alert the operator.

15 Case Study Uber Self-Driving Vehicle Sensors detected something but were confused about what it was System did not react or warn driver to react AEB was deactivated, unbeknownst to driver All aspects of the self-driving system were operating normally at the time of the crash, and there were no faults or diagnostic messages. - NTSB Typical, attentive driver likely would have avoided the collision