Verification and validation methods for decision-making and planning of automated vehicles: A review
Verification and validation (V&V) hold a significant position in the research and development
of automated vehicles (AVs). Current literature indicates that different V&V techniques have …
of automated vehicles (AVs). Current literature indicates that different V&V techniques have …
A survey on trajectory-prediction methods for autonomous driving
In order to drive safely in a dynamic environment, autonomous vehicles should be able to
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …
Artificial empathy in marketing interactions: Bridging the human-AI gap in affective and social customer experience
Artificial intelligence (AI) continues to transform firm-customer interactions. However, current
AI marketing agents are often perceived as cold and uncaring and can be poor substitutes …
AI marketing agents are often perceived as cold and uncaring and can be poor substitutes …
Machine learning for autonomous vehicle's trajectory prediction: A comprehensive survey, challenges, and future research directions
The significant contribution of human errors, accounting for approximately 94%(with a
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
Driving behavior modeling using naturalistic human driving data with inverse reinforcement learning
Driving behavior modeling is of great importance for designing safe, smart, and
personalized autonomous driving systems. In this paper, an internal reward function-based …
personalized autonomous driving systems. In this paper, an internal reward function-based …
Highway decision-making and motion planning for autonomous driving via soft actor-critic
In this study, a decision-making and motion planning controller with continuous action space
is constructed in the highway driving scenario based on deep reinforcement learning. In the …
is constructed in the highway driving scenario based on deep reinforcement learning. In the …
Interaction-aware trajectory prediction and planning for autonomous vehicles in forced merge scenarios
Merging is, in general, a challenging task for both human drivers and autonomous vehicles,
especially in dense traffic, because the merging vehicle typically needs to interact with other …
especially in dense traffic, because the merging vehicle typically needs to interact with other …
Safety assurances for human-robot interaction via confidence-aware game-theoretic human models
An outstanding challenge with safety methods for human-robot interaction is reducing their
conservatism while maintaining robustness to variations in human behavior. In this work, we …
conservatism while maintaining robustness to variations in human behavior. In this work, we …
Scalable inverse reinforcement learning through multifidelity Bayesian optimization
Data in many practical problems are acquired according to decisions or actions made by
users or experts to achieve specific goals. For instance, policies in the mind of biologists …
users or experts to achieve specific goals. For instance, policies in the mind of biologists …
Off-policy inverse Q-learning for discrete-time antagonistic unknown systems
This paper proposes a data-driven model-free inverse reinforcement learning (RL) algorithm
to reconstruct the unknown cost function of the demonstrated discrete-time (DT) dynamical …
to reconstruct the unknown cost function of the demonstrated discrete-time (DT) dynamical …