UC Santa Cruz

Autonomous Vehicles (AVs) will share the roads with humans, where they will regularly interact with different participating agents such as human-driven vehicles, cyclists, pedestrians, etc. Scenario-based testing is a simulation-based AV testing process where many possible scenarios can be tested inside a simulator to verify that an AV interacts appropriately in corresponding scenarios. According to the current approaches, the participating vehicles in those scenarios are modeled using static, predetermined, time-stamped trajectory information, which fails to obtain the behavioral variability of human drivers. Moreover, such a modeling approach limits the usability of the scenario with continuous software updates of the AVs, as surrounding vehicles remain static while the AVs behavior changes due to system updates. To address this issue, we created CogMod, a cognitive theory-inspired human driver behavior model built on a cognitive framework that integrates two complimentary cognitive architectures, QN-MHP and ACT-R, to reflect human cognition while driving. Contrary to most models, where control is directly based on observed variables, the control actions of CogMod agents rely on a temporally persistent internal representation. This internal representation results from a novel gaze mechanism that enables the CogMod driver to have a selective update of the surrounding environment. The model can simulate human drivers' perceptive and cognitive limitations and thereby capture human driving variability. We put our model through two different simulations to test its ability to generate variable driving behavior. In the first simulation, we explored the influence of decision-making latency, a consequence of variable cognitive processing capacity. This variation, simulated by our model and facilitated by the hybrid cognitive architecture, was evaluated based on the distribution of stopping distances under differing cognitive processing capacities. In the second simulation, we test CogMod's ability to augment real-world naturalistic driving data. To test the model's ability to generate a variety of scenarios from a given scenario, we used car-following scenarios from the HighD dataset. The microscopic distributions obtained from the naturalistic dataset are compared with the simulation results using agents based on Intelligent Driver Model and CogMod. Our results show that with variable driving behavior, our model can augment an existing scenario and increase its complexity.