SUBJECT: Ph.D. Proposal Presentation
   
BY: Francis Fish
   
TIME: Wednesday, March 24, 2021, 9:00 a.m.
   
PLACE: Bluejeans, 000
   
TITLE: Biologically Inspired Connected Advanced Driver Assistance Systems
   
COMMITTEE: Dr. Berdinus Bras, Chair (ME)
Dr. Marc Weissburg (ES&T)
Dr. Roger Jiao (ME)
Dr. Anirban Mazumdar (ME)
Dr. Jessica Cicchino (External)
 

SUMMARY

Advanced Driver Assistance Systems (ADAS) have become commonplace in the automotive industry over the last few decades. ADAS has in some instances been shown to significantly reduce the number and severity of accidents. There are a number of directions ADAS could be further progressed. A number of ADAS systems have already been improved from passive, alert or warning, systems to active systems which provide early warning and if no action is taken will control the vehicle to avoid a collision or reduce the impact of the collision. Studies about the individual ADAS technologies have found significant improvement for reduction in collisions, but when evaluating the actual vehicles driving the performance of ADAS has been fairly constant since 2015. At the same time, industry is looking at networking vehicle ADAS with fixed infrastructure or with other vehicles’ ADAS. The ultimate goal of ADAS and connected ADAS is the development of autonomous vehicles.
Biologically inspired systems provide an intriguing avenue for examination by applying self-organization found in biological communities to connecting ADAS among vehicles and fixed systems. Biological systems developed over millions of years to become highly organized and efficient. Biological inspiration has been used with much success in several engineering and science disciplines to optimize processes and designs. Applying movement patterns found in nature to automotive transportation is a rational progression.
This proposal strategizes how to further the effectiveness of ADAS through the connection of ADAS with supporting assets both fixed systems and other vehicles with ADAS based on biological inspiration. The connection priorities will be refined by the relative positioning of the assets interacting with a particular vehicle’s ADAS. Then based on the relative positioning data distribution among systems will be stratified based on level of relevance.
This proposal’s corresponding dissertation will contribute to the present understanding of ADAS effectiveness in real world situations and set forth a method for how to optimally connect local ADAS vehicles following from biological inspiration. Also, there will be a better understanding of how ADAS reduces accidents and injury severity. The method for how to structure an ADAS network will provide a framework for auto-manufacturers for the development of their proprietary networked ADAS.
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