Modern safety systems are transforming vehicles from human-controlled passive devices into human-centric intelligent/ active systems. There is a wide range of systems from fully autonomous vehicles to human-augmented control devices which have emerged in this field. In current trends, co-operative active systems that have the driver in the decision and control processes are favoured for their 'human-centric' approach. However, these systems pose a challenge in the design process since obtaining reliable human behavior models are difficult due to the complex nature of driving task in adynamic traffic environment. From a control theory perspective, driving can be seen as a combination of continuous control segments combined with adiscrete decision process. In this study, we will model driver behavior utilizing Hybrid Dynamic Systems (HDS) combining stochastic modeling tools (such as Hidden Markov Models) with control theoretic models. A subset of CAN-Bus and video channels from a demographically balanced UTDrive Corpus containing video (2 channels: driver and road scene), audio, and CAN-Bus signals of realistic driving sessions for 77 drivers are used to verify HDSmodels of lateral and longitudinal control behaviour. The model is used to suggest a 'driver-aware' active safety system capable of assisting the driver in several lateral control tasks; lane-keeping, curve-negotiation and lane changing. The full text of this paper may be found at: http://www-nrd.nhtsa.dot.gov/pdf/esv/esv21/09-0200.pdf For the covering abstract see ITRD E145407.
Abstract