Adaptive Intelligence for Cyber-­Physical Automotive Active Safety System Design and Evaluation

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Abstract:

In order to improve the current capabilities of automotive active safety control systems (ASCS) one needs to take into account the interactions between driver/vehicle/ASCS/environment. To achieve this goal, this research will infer longterm and short-term driver behavior via the use of Bayesian networks and neuromorphic algorithms to estimate the driver’s skills and current state of attention from eye movement data, together with dynamic motion cues obtained from steering and pedal inputs. This information will be injected into the ASCS to enhance its performance, by taking advantage of recent results from adaptive, real-time, model-predictive optimal control. The correct level of autonomy and workload distribution between the driver and ASCS will ensure that no conflicts arise between the driver and the control system and the safety and passenger comfort are not compromised. A comprehensive plan is proposed to test and validate the developed theory

  • active safety systems
  • Adaptation
  • cognitive driver modeling
  • computational neuroscience
  • learning
  • 1544814
  • Automotive
  • CPS Domains
  • Testing
  • Control
  • Modeling
  • Critical Infrastructure
  • Real-Time Coordination
  • Simulation
  • Transportation
  • Validation and Verification
  • Foundations
  • National CPS PI Meeting 2015
  • 2015
  • Abstract
  • Poster
  • Academia
  • 2015 CPS PI MTG Videos, Posters, and Abstracts
Submitted by Panagiotis Tsiotras on