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Found 21 papers, 1 papers with code
CausalAgents: A Robustness Benchmark for Motion Forecasting using Causal Relationships
Finally, we release the causal agent labels (at https://github. com/google-research/causal-agents) as an additional attribute to WOMD and the robustness benchmarks to aid the community in building more reliable and safe deep-learning models for motion forecasting.
Transferable and Adaptable Driving Behavior Prediction
By mimicking humans' cognition model and semantic understanding during driving, we propose HATN, a hierarchical framework to generate high-quality, transferable, and adaptable predictions for driving behaviors in multi-agent dense-traffic environments.
A Microscopic Pandemic Simulator for Pandemic Prediction Using Scalable Million-Agent Reinforcement Learning
To efficiently simulate with massive amounts of agents in MPS, we propose Scalable Million-Agent DQN (SMADQN).
On complementing end-to-end human behavior predictors with planning
High capacity end-to-end approaches for human motion (behavior) prediction have the ability to represent subtle nuances in human behavior, but struggle with robustness to out of distribution inputs and tail events.
Learning Human Rewards by Inferring Their Latent Intelligence Levels in Multi-Agent Games: A Theory-of-Mind Approach with Application to Driving Data
In this work, we advocate that humans are bounded rational and have different intelligence levels when reasoning about others' decision-making process, and such an inherent and latent characteristic should be accounted for in reward learning algorithms.
Diverse Critical Interaction Generation for Planning and Planner Evaluation
Experiments show that our model can generate diverse interactions in various scenarios.
A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning
To address this challenge, we propose a hierarchical behavior planning framework with a set of low-level safe controllers and a high-level reinforcement learning algorithm (H-CtRL) as a coordinator for the low-level controllers.
IDE-Net: Interactive Driving Event and Pattern Extraction from Human Data
We find three interpretable patterns of interactions, bringing insights for driver behavior representation, modeling and comprehension.
Socially-Compatible Behavior Design of Autonomous Vehicles with Verification on Real Human Data
It allows the AVs to infer the characteristics of other road users online and generate behaviors optimizing not only their own rewards, but also their courtesy to others, and their confidence regarding the prediction uncertainties.
Bounded Risk-Sensitive Markov Games: Forward Policy Design and Inverse Reward Learning with Iterative Reasoning and Cumulative Prospect Theory
Classical game-theoretic approaches for multi-agent systems in both the forward policy design problem and the inverse reward learning problem often make strong rationality assumptions: agents perfectly maximize expected utilities under uncertainties.
Expressing Diverse Human Driving Behavior with Probabilistic Rewards and Online Inference
In human-robot interaction (HRI) systems, such as autonomous vehicles, understanding and representing human behavior are important.
Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning with Application to Autonomous Driving
Different from existing IRL algorithms, by introducing an efficient continuous-domain trajectory sampler, the proposed algorithm can directly learn the reward functions in the continuous domain while considering the uncertainties in demonstrated trajectories from human drivers.
INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps
3) The driving behavior is highly interactive and complex with adversarial and cooperative motions of various traffic participants.
Generic Prediction Architecture Considering both Rational and Irrational Driving Behaviors
Both rational and irrational behaviors exist, and the autonomous vehicles need to be aware of this in their prediction module.
Interpretable Modelling of Driving Behaviors in Interactive Driving Scenarios based on Cumulative Prospect Theory
Hence, the goal of this work is to formulate the human drivers' behavior generation model with CPT so that some ``irrational'' behavior or decisions of human can be better captured and predicted.
Behavior Planning of Autonomous Cars with Social Perception
The uncertainties can come either from sensor limitations such as occlusions and limited sensor range, or from probabilistic prediction of other road participants, or from unknown social behavior in a new area.
Multi-modal Probabilistic Prediction of Interactive Behavior via an Interpretable Model
The proposed method is based on a generative model and is capable of jointly predicting sequential motions of each pair of interacting agents.
Towards a Fatality-Aware Benchmark of Probabilistic Reaction Prediction in Highly Interactive Driving Scenarios
Modified methods based on PGM, NN and IRL are provided to generate probabilistic reaction predictions in an exemplar scenario of nudging from a highway ramp.
Probabilistic Prediction of Interactive Driving Behavior via Hierarchical Inverse Reinforcement Learning
To safely and efficiently interact with other road participants, AVs have to accurately predict the behavior of surrounding vehicles and plan accordingly.
Courteous Autonomous Cars
Such a courtesy term enables the robot car to be aware of possible irrationality of the human behavior, and plan accordingly.
A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning
For safe and efficient planning and control in autonomous driving, we need a driving policy which can achieve desirable driving quality in long-term horizon with guaranteed safety and feasibility.
