NCAP and Thatcham New Assisted Driving Testing is Dangerously Deficient and Misleading
Recently NCAP and Thatcham published their L1 ADAS Highway Assisted Driving results. While efforts like this are necessary, and several important areas were included, there are two areas of significant concern. These two flaws will lead to significantly flawed assumptions on the public’s part, false confidence, and very avoidable tragedies.
https://www.cnet.com/roadshow/news/tesla-model-3-ncap-assisted-driving-assessment/
Highway ODD — The report clearly states this is for highway ODD or geofence ADAS. However, much of the data being portrayed to the public domain does not. Without the word “highway” clearly called out most people will think this is for all ODDs.
Stationary/Crossing Objects –Tests for stationary and crossing objects were not included. The testing does not include stationary and crossing object scenarios or detection. This is extremely problematic, especially given Tesla’s performance in this area. Tesla’s have run into several stationary objects on the highway. Killing several people. Vehicles, barriers, trailers etc. (Any day now one will run through a construction zone.) This appears to be due to not having LiDAR, relying way too much on camera technology and a radar that doesn’t have the scanning capability to fill that gap. While perfectly perpendicular crossing objects on a highway are probably rare, stationary objects are not. This test should be added for all relevant vehicles, especially Tesla.
More on my POV in the articles below
The Autonomous Vehicle Industry can be Saved by doing the Opposite of what is being done now
SAE Autonomous Vehicle Engineering Magazine — Simulation’s Next Generation (featuring Dactle)
· https://www.sae.org/news/2020/08/new-gen-av-simulation
The Crash of the Autonomous Vehicle Industry
· https://medium.com/predict/the-crash-of-the-autonomous-vehicle-industry-f71fd26c1ed0
Proposal for Successfully Creating an Autonomous Ground or Air Vehicle
Autonomous Vehicles Need to Have Accidents to Develop this Technology
Using the Real World is better than Proper Simulation for AV Development — NONSENSE
The Hype of Geofencing for Autonomous Vehicles
My name is Michael DeKort — I am a former system engineer, engineering and program manager for Lockheed Martin. I worked in aircraft simulation, the software engineering manager for all of NORAD, the Aegis Weapon System, and on C4ISR for DHS.
Key Industry Participation
- Lead — SAE On-Road Autonomous Driving SAE Model and Simulation Task
- Member SAE ORAD Verification and Validation Task Force
- Member DIN/SAE International Alliance for Mobility Testing & Standardization (IAMTS) Sensor Simulation Specs
- Stakeholder for UL4600 — Creating AV Safety Guidelines
- Member of the IEEE Artificial Intelligence & Autonomous Systems Policy Committee (AI&ASPC)
- Presented the IEEE Barus Ethics Award for Post 9/11 Efforts
My company is Dactle
We are building an aerospace/DoD/FAA level D, full L4/5 simulation-based testing and AI system with an end-state scenario matrix to address several of the critical issues in the AV/OEM industry I mentioned in my articles below. This includes replacing 99.9% of public shadow and safety driving. As well as dealing with significant real-time, model fidelity and loading/scaling issues caused by using gaming engines and other architectures. (Issues Unity will confirm. We are now working together. We are also working with UAV companies). If not remedied these issues will lead to false confidence and performance differences between what the Plan believes will happen and what actually happens. If someone would like to see a demo or discuss this further please let me know.
