How Do Human-Driven Vehicles Avoid Pedestrians in Interactive Environments? A Naturalistic Driving Study

Abstract

One of the major challenges for autonomous vehicles (AVs) is how to drive in shared pedestrian environments. AVs cannot make their decisions and behaviour human-like or natural when they encounter pedestrians with different crossing intentions. The main reasons for this are the lack of natural driving data and the unclear rationale of the human-driven vehicle and pedestrian interaction. This paper aims to understand the underlying behaviour mechanisms using data of pedestrian–vehicle interactions from a naturalistic driving study (NDS). A naturalistic driving test platform was established to collect motion data of human-driven vehicles and pedestrians. A manual pedestrian intention judgment system was first developed to judge the pedestrian crossing intention at every moment in the interaction process. A total of 98 single pedestrian crossing events of interest were screened from 1274 pedestrian–vehicle interaction events under naturalistic driving conditions. Several performance metrics with quantitative data, including TTC, subjective judgment on pedestrian crossing intention (SJPCI), pedestrian position and crossing direction, and vehicle speed and deceleration were analyzed and applied to evaluate human-driven vehicles’ yielding behaviour towards pedestrians. The results show how vehicles avoid pedestrians in different interaction scenarios, which are classified based on vehicle deceleration. The behaviour and intention results are needed by future AVs, to enable AVs to avoid pedestrians more naturally, safely, and smoothly.

Social Interaction‐Aware Dynamical Models and Decision‐Making for Autonomous Vehicles

“…The results in Ref. [25] show that the vehicle deceleration behaviour is relative to the initial Time To Collision (TTC), subjective judgment of pedestrian crossing intention, vehicle speed, pedestrian position and crossing direction.…”

Would you cross the road? Modelling interactions between the factors influencing pedestrians’ decisions when exposed to automated vehicles

“…This objective can form another research study which analyzes pedestrian decisions that can be analyzed based on the gap acceptance of the pedestrians [8,28] or decision-making [6] and response time. Such data provides insight into the factors that need to be taken into account by an AV so that it can make an appropriate estimation on pedestrian crossing behaviour [18,26].…”