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In intelligent vehicle-infrastructure cooperation systems (i-VICS), the driving risk field is an effective method for evaluating the driving safety of connected and automated vehicles (CAVs). However, existing driving risk field models do not consider the geometric characteristics and heading angles of vehicles and ignore the influences of the ego vehicle, which limits the accuracy of these existing models for vehicle safety assessments. This paper describes an extended driving risk field model. This driving risk field model includes the time to collision (TTC) and adds the physical attributes and movement information of the ego vehicle, including the vehicle size and heading, into the driving risk field model which improves the safety assessment. Application of this driving risk field model to typical traffic scenarios shows that this extended model overcomes the limitations of existing models. Simulations using this model for …