Zobrazeno 1 - 10
of 463
pro vyhledávání: '"Liu, Henry X."'
This paper studies the traffic state estimation problem at signalized intersections with low penetration rate vehicle trajectory data. While many existing studies have proposed different methods to estimate unknown traffic states and parameters (e.g.
Externí odkaz:
http://arxiv.org/abs/2404.08667
Autor:
Zhang, Rusheng, Meng, Depu, Shen, Shengyin, Wang, Tinghan, Karir, Tai, Maile, Michael, Liu, Henry X.
Roadside perception systems are increasingly crucial in enhancing traffic safety and facilitating cooperative driving for autonomous vehicles. Despite rapid technological advancements, a major challenge persists for this newly arising field: the abse
Externí odkaz:
http://arxiv.org/abs/2401.12392
Real-time safety metrics are important for the automated driving system (ADS) to assess the risk of driving situations and to assist the decision-making. Although a number of real-time safety metrics have been proposed in the literature, systematic p
Externí odkaz:
http://arxiv.org/abs/2401.01501
As vehicular communication and networking technologies continue to advance, infrastructure-based roadside perception emerges as a pivotal tool for connected automated vehicle (CAV) applications. Due to their elevated positioning, roadside sensors, in
Externí odkaz:
http://arxiv.org/abs/2310.05290
Recently, advancements in vehicle-to-infrastructure communication technologies have elevated the significance of infrastructure-based roadside perception systems for cooperative driving. This paper delves into one of its most pivotal challenges: data
Externí odkaz:
http://arxiv.org/abs/2306.17302
Traffic conflicts have been studied by the transportation research community as a surrogate safety measure for decades. However, due to the rarity of traffic conflicts, collecting large-scale real-world traffic conflict data becomes extremely challen
Externí odkaz:
http://arxiv.org/abs/2303.00563
Safety performance evaluation is critical for developing and deploying connected and automated vehicles (CAVs). One prevailing way is to design testing scenarios using prior knowledge of CAVs, test CAVs in these scenarios, and then evaluate their saf
Externí odkaz:
http://arxiv.org/abs/2212.00517
Testing and evaluation is a critical step in the development and deployment of connected and automated vehicles (CAVs). Due to the black-box property and various types of CAVs, how to test and evaluate CAVs adaptively remains a major challenge. Many
Externí odkaz:
http://arxiv.org/abs/2207.09259
Autor:
Liu, Henry X., Feng, Shuo
In this paper, we reveal that the rarity of safety-critical events in high-dimensional driving environments is the root cause of the safety challenge for autonomous vehicle development. We formulate it as "curse of rarity" (CoR) because it occurs ubi
Externí odkaz:
http://arxiv.org/abs/2207.02749
Autor:
Zou, Zhengxia, Zhang, Rusheng, Shen, Shengyin, Pandey, Gaurav, Chakravarty, Punarjay, Parchami, Armin, Liu, Henry X.
We propose a novel and pragmatic framework for traffic scene perception with roadside cameras. The proposed framework covers a full-stack of roadside perception pipeline for infrastructure-assisted autonomous driving, including object detection, obje
Externí odkaz:
http://arxiv.org/abs/2206.09770