How To Evaluate the Navigation of Autonomous Vehicles Around Pedestrians?

Abstract

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).

Reliable Risk Assessment and Management using Probabilistic Fusion of Predictive Inter-Distance Profile for Urban Autonomous Driving

“…Since the fusion of the feature points d(t)=:F _P IDP (t) in ( 7) are known, the parameters of the fusion {a 0 , a 1 , a 2 } can be solved for using the matrix (or pseudo) inverse. The resulting parameters are then substituted in (6) to get a single unified F-PIDP curve that encapsulates the combined information as shown in Figure 4.…”

“…Many collision-based risk assessment metrics have been proposed and leveraged in the literature, authors in [5] give a summary of the state of the art on criticality metrics and the relationship between them with regards to desired applications. Similarly, multiple performance metrics are presented in [6] for evaluating an autonomous vehicle moving in a shared space with pedestrians. The Time To Collision (TTC) [7] is the most popular risk indicator because of its simplicity and computational efficiency.…”