Search Results for author: Liting Sun

Found 20 papers, 0 papers with code

Transferable and Adaptable Driving Behavior Prediction

no code implementations10 Feb 2022 Letian Wang, Yeping Hu, Liting Sun, Wei Zhan, Masayoshi Tomizuka, Changliu Liu

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.

Autonomous Vehicles Trajectory Prediction

On complementing end-to-end human behavior predictors with planning

no code implementations9 Mar 2021 Liting Sun, Xiaogang Jia, Anca D. Dragan

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.

Autonomous Driving Human motion prediction +1

Learning Human Rewards by Inferring Their Latent Intelligence Levels in Multi-Agent Games: A Theory-of-Mind Approach with Application to Driving Data

no code implementations7 Mar 2021 Ran Tian, Masayoshi Tomizuka, Liting Sun

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.

Decision Making reinforcement-learning

A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning

no code implementations17 Jan 2021 Jinning Li, Liting Sun, Jianyu Chen, Masayoshi Tomizuka, Wei Zhan

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.

Autonomous Vehicles reinforcement-learning

IDE-Net: Interactive Driving Event and Pattern Extraction from Human Data

no code implementations4 Nov 2020 Xiaosong Jia, Liting Sun, Masayoshi Tomizuka, Wei Zhan

We find three interpretable patterns of interactions, bringing insights for driver behavior representation, modeling and comprehension.

Autonomous Vehicles Multi-Task Learning

Socially-Compatible Behavior Design of Autonomous Vehicles with Verification on Real Human Data

no code implementations28 Oct 2020 Letian Wang, Liting Sun, Masayoshi Tomizuka, Wei Zhan

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.

Autonomous Vehicles

Bounded Risk-Sensitive Markov Games: Forward Policy Design and Inverse Reward Learning with Iterative Reasoning and Cumulative Prospect Theory

no code implementations3 Sep 2020 Ran Tian, Liting Sun, Masayoshi Tomizuka

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

no code implementations20 Aug 2020 Liting Sun, Zheng Wu, Hengbo Ma, Masayoshi Tomizuka

In human-robot interaction (HRI) systems, such as autonomous vehicles, understanding and representing human behavior are important.

Autonomous Vehicles

Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning with Application to Autonomous Driving

no code implementations22 Jun 2020 Zheng Wu, Liting Sun, Wei Zhan, Chenyu Yang, Masayoshi Tomizuka

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.

Autonomous Driving reinforcement-learning

Generic Prediction Architecture Considering both Rational and Irrational Driving Behaviors

no code implementations23 Jul 2019 Yeping Hu, Liting Sun, Masayoshi Tomizuka

Both rational and irrational behaviors exist, and the autonomous vehicles need to be aware of this in their prediction module.

Autonomous Vehicles

Interpretable Modelling of Driving Behaviors in Interactive Driving Scenarios based on Cumulative Prospect Theory

no code implementations19 Jul 2019 Liting Sun, Wei Zhan, Yeping Hu, Masayoshi Tomizuka

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.

Autonomous Vehicles Decision Making

Behavior Planning of Autonomous Cars with Social Perception

no code implementations2 May 2019 Liting Sun, Wei Zhan, Ching-Yao Chan, Masayoshi Tomizuka

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.

Autonomous Vehicles Decision Making

Multi-modal Probabilistic Prediction of Interactive Behavior via an Interpretable Model

no code implementations22 Mar 2019 Yeping Hu, Wei Zhan, Liting Sun, Masayoshi Tomizuka

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

no code implementations10 Sep 2018 Wei Zhan, Liting Sun, Yeping Hu, Jiachen Li, Masayoshi Tomizuka

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.

Autonomous Vehicles Decision Making

Probabilistic Prediction of Interactive Driving Behavior via Hierarchical Inverse Reinforcement Learning

no code implementations9 Sep 2018 Liting Sun, Wei Zhan, Masayoshi Tomizuka

To safely and efficiently interact with other road participants, AVs have to accurately predict the behavior of surrounding vehicles and plan accordingly.

Autonomous Vehicles reinforcement-learning

Courteous Autonomous Cars

no code implementations8 Aug 2018 Liting Sun, Wei Zhan, Masayoshi Tomizuka, Anca D. Dragan

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

no code implementations9 Jul 2017 Liting Sun, Cheng Peng, Wei Zhan, Masayoshi Tomizuka

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.

Autonomous Driving Imitation Learning

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