Learning human rewards by inferring their latent intelligence levels in multi-agent games: A theory-of-mind approach with application to driving data
Reward function, as an incentive representation that recognizes humans' agency and
rationalizes humans' actions, is particularly appealing for modeling human behavior in …
rationalizes humans' actions, is particularly appealing for modeling human behavior in …
Anticipatory Planning: Improving Long-Lived Planning by Estimating Expected Cost of Future Tasks
R Dhakal, MRH Talukder… - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
We consider a service robot in a household environment given a sequence of high-level
tasks one at a time. Most existing task planners, lacking knowledge of what they may be …
tasks one at a time. Most existing task planners, lacking knowledge of what they may be …
A game-theoretic strategy-aware interaction algorithm with validation on real traffic data
Interactive decision-making and motion planning are important to safety-critical autonomous
agents, particularly when they interact with humans. Many different interaction strategies can …
agents, particularly when they interact with humans. Many different interaction strategies can …
Human-like mechanism deep learning model for longitudinal motion control of autonomous vehicles
Z Gao, T Yu, F Gao, R Zhao, T Sun - Engineering Applications of Artificial …, 2024 - Elsevier
Artificial intelligence (AI) plays a critical role in the prediction, planning, and control of
autonomous vehicle. The original motion control methods are increasing in accuracy, but …
autonomous vehicle. The original motion control methods are increasing in accuracy, but …
Adaptive sampling-based motion planning with a non-conservatively defensive strategy for autonomous driving
Sampling-based motion planning methods are widely adopted in autonomous driving.
Typically, sampling can be decoupled into two layers: a path sampling layer and a speed …
Typically, sampling can be decoupled into two layers: a path sampling layer and a speed …
Mathematical models of human drivers using artificial risk fields
In this paper, we use the concept of artificial risk fields to predict how human operators
control a vehicle in response to upcoming road situations. A risk field assigns a non …
control a vehicle in response to upcoming road situations. A risk field assigns a non …
Calibration of human driving behavior and preference using vehicle trajectory data
In a recent work (Dai et al., 2021) we proposed a multi-agent computational framework in
which each agent's driving policy at micro-level is derived by maximizing its own utility …
which each agent's driving policy at micro-level is derived by maximizing its own utility …
How shall I drive? Interaction modeling and motion planning towards empathetic and socially-graceful driving
While intelligence of autonomous vehicles (AVs) has significantly advanced in recent years,
accidents involving AVs suggest that these autonomous systems lack gracefulness in driving …
accidents involving AVs suggest that these autonomous systems lack gracefulness in driving …
Subtle motion cues by automated vehicles can nudge human drivers' decisions: Empirical evidence and computational cognitive model
Automated vehicles (AVs) can bring about numerous benefits for society but they are
unprepared to enter our roads yet, in large part because of the difficulties in interacting with …
unprepared to enter our roads yet, in large part because of the difficulties in interacting with …
Implementation of road safety perception in autonomous vehicles in a lane change scenario
E Del Re, C Olaverri-Monreal - 2022 IEEE International …, 2022 - ieeexplore.ieee.org
Understanding human driving behavior is crucial to develop autonomous vehicles'
algorithms. However, most low level automation, such as the one in advanced driving …
algorithms. However, most low level automation, such as the one in advanced driving …