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Predicting Drivers' Takeover Readiness and Designing Adaptive In-Vehicle Alert System

Highly automated driving (HAD) is becoming an engineering reality. In HAD, the driver’s role will transform from an operator to a system supervisor making it possible for him to engage in non-driving-related activities. If the automated vehicle reaches its system limit, the driver will be required to resume control of the vehicle in a certain amount of time. Despite the promising safety benefits of HAD, the concern from a human factors perspective is that drivers become increasingly out-of-the-loop once they start to engage in non-driving-related tasks. Drivers decoupled from the operational level of control have difficulty taking over in any situation, and particularly in situations that the automation is not able to handle.

To tackle this problem, recent human factors investigations into drivers’ takeover in HAD have explored the design of in-vehicle alert system, especially the optimal takeover-request lead time (TORlt). These studies shed light on the design of in-vehicle alert systems. Nevertheless, they are subject to one major limitation: Existing research has not taken into full consideration the contextualization and personalization of in-vehicle alert system, despite the fact that drivers’ takeover performance in HAD can be influenced by various factors, including engagement in non-driving-related tasks and workload, driver’s age, and driving scenario complexity. 

In this project, the researchers aim to:

  1. Define the indicator takeover readiness that describes how able the driver is to take over control of the vehicle in any situation
  2. Develop computational models capable of predicting drivers’ takeover readiness by analyzing both the driver’s physiological data and data from the current driving scenario in real time
  3. Design and evaluate an adaptive in-vehicle alert system.

 

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The amount of the award is $300,000 for UMSI for the project period. The grant is funded by Mcity.