The navigation of autonomous vehicles around pedestrians is a key challenge when driving in urban environments. It is essential to test the proposed navigation system using simulation before moving to real-life implementation and testing. Evaluating the performance of the system requires the design of a diverse set of tests which spans the targeted working scenarios and conditions. These tests can then undergo a process of evaluation using a set of adapted performance metrics. This work addresses the problem of performance evaluation for an autonomous vehicle in a shared space with pedestrians. The methodology for designing the test simulations is discussed. Moreover, a group of performance metrics is proposed to evaluate the different aspects of the navigation: the motion safety, the quality of the generated trajectory and the comfort of the pedestrians surrounding the vehicle. Furthermore, the success/fail criterion for each metric is discussed. The implementation of the proposed evaluation method is illustrated by evaluating the performance of a pre-designed proactive navigation system using a shared space crowd simulator under Robot Operating System (ROS).

Navigation in close proximity with pedestrians is a challenge on the way to fully automated vehicles. Pedestrian-friendly navigation requires an understanding of pedestrian reaction and intention. Merely safety based reactive systems can lead to sub-optimal navigation solutions resulting in the freezing of the vehicle in many scenarios. Moreover, a strictly reactive method can produce unnatural driving patterns which cannot guarantee the legibility or social acceptance of the automated vehicle. This work presents a proactive maneuvering method adapted to navigation in close interaction with pedestrians using a dynamic channel approach. The method allows to proactively explore the navigation options based on anticipating pedestrians cooperation. The navigation is tested in frontal and lateral crossing scenarios with variable space density. The system is implemented under ROS, and compared with the probabilistic Risk-RRT planning method. The results are evaluated based on the safety and comfort of the pedestrians, and the quality of the vehicle's trajectory.

Developing autonomous vehicles capable of navigating safely and socially around pedestrians is a major challenge in intelligent transportation. This challenge cannot be met without understanding pedestrians' behavioral response to an autonomous vehicle, and the task of building a clear and quantitative description of the pedestrian to vehicle interaction remains a key milestone in autonomous navigation research. As a step towards safe proactive navigation in a space shared with pedestrians, this work introduces a pedestrian-vehicle interaction behavioral model. The model estimates the pedestrian's cooperation with the vehicle in an interaction scenario by a quantitative time-varying function. Using this cooperation estimation the pedestrian's trajectory is predicted by a cooperation-based trajectory planning model. Both parts of the model are tested and validated using real-life recorded scenarios of pedestrian-vehicle interaction. The model is capable of describing and predicting agents' behaviors when interacting with a vehicle in both lateral and frontal crossing scenarios.