Shared Situational Awareness for Connected and Automated Vehicles in Urban Scenarios

QC 20260408

Abstract

A major challenge in developing connected and automated vehicles~(CAVs) for urban environments is achieving a comprehensive understanding of the surrounding traffic scene. This relies on situational awareness, defined as the ability to perceive, interpret, and anticipate the behavior of surrounding road-users, which is essential to ensure safety. In particular, unprotected road-users, such as pedestrians and cyclists, are often occluded or located in sensor blind-spots of the CAV, which remains a critical challenge. This thesis aims to improve the situational awareness of the ego-vehicle, the CAV of primary interest, in urban environments by leveraging vehicle-to-everything (V2X) communication to incorporate information from connected road-users. A framework using set-based methods is developed to systematically handle uncertainties in measurements and initial conditions of detected pedestrians.

The objective is to address several key challenges that arise in real-world scenarios, including data inconsistency, data association, pedestrian motion prediction, and efficient reduction of redundant information. The thesis first proposes a shared situational awareness framework for occluded pedestrian-crossing scenario to compute an estimated set for the pedestrian. The framework is extended to handle measurements from V2X units that may be inconsistent with the ground truth of the detected pedestrian. To address scenarios involving multiple occluded pedestrians, a data association method based on intersection-over-union heuristics is introduced. Pedestrian motion prediction is further studied using both a data-driven approach and a bounded velocity–acceleration model applied to the estimated set obtained from the framework. An occlusion-aware extension is also developed to handle situations where occlusions affect both the ego-vehicle and V2X units by exploiting previously observed measurements. Finally, a method for selecting and filtering relevant information from multiple V2X units is proposed to reduce the computational load while maintaining effectiveness. The proposed methods are validated through numerical simulations and real-world experiments using Scania prototype automated vehicles.

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