Trust in Automated Vehicle: A Meta-Analysis

Zhang, Zhengming; Tian, Renran; Duffy, Vincent G.

doi:10.1007/978-3-031-10784-9_13

Cited by 12 publications

(3 citation statements)

References 57 publications

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“…However, the adoption of connectivity technology faces significant challenges from the increasing complexity in analyzing system behavior and ensuring its safety, especially with the wider usage of neural network based components in vehicle decision making [42]. During the transition period to the next-generation transportation system, a mixed traffic stream of human-driven and autonomous vehicles [38], and connected and non-connected vehicles need to share the transportation network. Recent progress has been made to ensure system safety when all vehicles are of the same type [8,15], or connectivity is not enabled [17,19,28,29], and only some works [19,28] can generalize to systems with neural network based components.…”

Section: Introductionmentioning

confidence: 99%

Connectivity Enhanced Safe Neural Network Planner for Lane Changing in Mixed Traffic

Jiao¹,

Zheng²,

Liang³

et al. 2023

Preprint

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Connectivity technology has shown great potentials in improving the safety and efficiency of transportation systems by providing information beyond the perception and prediction capabilities of individual vehicles. However, it is expected that human-driven and autonomous vehicles, and connected and non-connected vehicles need to share the transportation network during the transition period to fully connected and automated transportation systems. Such mixed traffic scenarios significantly increase the complexity in analyzing system behavior and quantifying uncertainty for highly interactive scenarios, e.g., lane changing. It is even harder to ensure system safety when neural network based planners are leveraged to further improve efficiency. In this work, we propose a connectivity-enhanced neural network based lane changing planner. By cooperating with surrounding connected vehicles in dynamic environment, our proposed planner will adapt its planned trajectory according to the analysis of a safe evasion trajectory. We demonstrate the strength of our planner design in improving efficiency and ensuring safety in various mixed traffic scenarios with extensive simulations. We also analyze the system robustness when the communication or coordination is not perfect.

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Section: Introductionmentioning

confidence: 99%

Connectivity Enhanced Safe Neural Network Planner for Lane Changing in Mixed Traffic

Jiao¹,

Zheng²,

Liang³

et al. 2023

Preprint

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“…Automated vehicles are expected to drastically reduce road accidents, minimize the workload associated with driving and increase traveling comfort, allowing drivers to engage in various activities (so-called "non-driving related tasks, " or NDRTs) while the car takes care of driving (Fagnant and Kockelman, 2015;Payre et al, 2016;Kyriakidis et al, 2017;Milakis et al, 2017;Van Nes and Duivernvoorden, 2017;Litman and Litman, 2023). At the same time, the introduction of more advanced automated driving technology creates new challenges for human-machine interaction-many of which can be traced back to the way users interact with the technology (Lee and See, 2004;Kyriakidis et al, 2017;Carsten and Martens, 2019;OVV, 2019;Wintersberger et al, 2021;NHTSA, 2022;Zhang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Trust in automated vehicles: constructs, psychological processes, and assessment

Walker,

Forster,

Hergeth

et al. 2023

Front. Psychol.

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There is a growing body of research on trust in driving automation systems. In this paper, we seek to clarify the way trust is conceptualized, calibrated and measured taking into account issues related to specific levels of driving automation. We find that: (1) experience plays a vital role in trust calibration; (2) experience should be measured not just in terms of distance traveled, but in terms of the range of situations encountered; (3) system malfunctions and recovery from such malfunctions is a fundamental part of this experience. We summarize our findings in a framework describing the dynamics of trust calibration. We observe that methods used to quantify trust often lack objectivity, reliability, and validity, and propose a set of recommendations for researchers seeking to select suitable trust measures for their studies. In conclusion, we argue that the safe deployment of current and future automated vehicles depends on drivers developing appropriate levels of trust. Given the potentially severe consequences of miscalibrated trust, it is essential that drivers incorporate the possibility of new and unexpected driving situations in their mental models of system capabilities. It is vitally important that we develop methods that contribute to this goal.

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“…Mercedes has recently begun selling their Level 3 self-driving system (defined by SAE International as Conditional Driving Automation) on their S-Class, marking a significant milestone as higher-level automated driving techniques enter ordinary people's lives. However, while Level 2 and 3 automated systems can drive autonomously under human supervision and within the Operational Design Domain (ODD), the main challenge for fully automated cars to safely and efficiently drive in urban settings remains interactions with pedestrians (Domeyer, Lee, and Toyoda 2020;Herman et al 2021;Zhang, Tian, and Duffy 2023).…”

Section: Introductionmentioning

confidence: 99%

TrEP: Transformer-Based Evidential Prediction for Pedestrian Intention with Uncertainty

Zhang

Tian

Ding

2023

AAAI

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With rapid development in hardware (sensors and processors) and AI algorithms, automated driving techniques have entered the public’s daily life and achieved great success in supporting human driving performance. However, due to the high contextual variations and temporal dynamics in pedestrian behaviors, the interaction between autonomous-driving cars and pedestrians remains challenging, impeding the development of fully autonomous driving systems. This paper focuses on predicting pedestrian intention with a novel transformer-based evidential prediction (TrEP) algorithm. We develop a transformer module towards the temporal correlations among the input features within pedestrian video sequences and a deep evidential learning model to capture the AI uncertainty under scene complexities. Experimental results on three popular pedestrian intent benchmarks have verified the effectiveness of our proposed model over the state-of-the-art. The algorithm performance can be further boosted by controlling the uncertainty level. We systematically compare human disagreements with AI uncertainty to further evaluate AI performance in confusing scenes. The code is released at https://github.com/zzmonlyyou/TrEP.git.

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Trust in Automated Vehicle: A Meta-Analysis

Cited by 12 publications

References 57 publications

Connectivity Enhanced Safe Neural Network Planner for Lane Changing in Mixed Traffic

Connectivity Enhanced Safe Neural Network Planner for Lane Changing in Mixed Traffic

Trust in automated vehicles: constructs, psychological processes, and assessment

TrEP: Transformer-Based Evidential Prediction for Pedestrian Intention with Uncertainty

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