Search Results for author: H. Eric Tseng

Found 18 papers, 4 papers with code

Autonomous Driving With Perception Uncertainties: Deep-Ensemble Based Adaptive Cruise Control

no code implementations22 Mar 2024 Xiao Li, H. Eric Tseng, Anouck Girard, Ilya Kolmanovsky

In the scenario of Adaptive Cruise Control (ACC), we employ the Deep Ensemble to estimate distance headway to the lead vehicle from RGB images and enable the downstream controller to account for the estimation uncertainty.

Autonomous Driving Decision Making +1

Game Projection and Robustness for Game-Theoretic Autonomous Driving

no code implementations29 Nov 2023 Mushuang Liu, H. Eric Tseng, Dimitar Filev, Anouck Girard, Ilya Kolmanovsky

This paper defines the robustness margin of a game solution as the maximum magnitude of cost function deviations that can be accommodated in a game without changing the optimality of the game solution.

Autonomous Driving Decision Making

Dream to Adapt: Meta Reinforcement Learning by Latent Context Imagination and MDP Imagination

no code implementations11 Nov 2023 Lu Wen, Songan Zhang, H. Eric Tseng, Huei Peng

Meta reinforcement learning (Meta RL) has been amply explored to quickly learn an unseen task by transferring previously learned knowledge from similar tasks.

Meta Reinforcement Learning

Interaction-Aware Decision-Making for Autonomous Vehicles in Forced Merging Scenario Leveraging Social Psychology Factors

no code implementations25 Sep 2023 Xiao Li, Kaiwen Liu, H. Eric Tseng, Anouck Girard, Ilya Kolmanovsky

Understanding the intention of vehicles in the surrounding traffic is crucial for an autonomous vehicle to successfully accomplish its driving tasks in complex traffic scenarios such as highway forced merging.

Autonomous Vehicles Decision Making

Safe Control and Learning Using Generalized Action Governor

no code implementations22 Nov 2022 Nan Li, Yutong Li, Ilya Kolmanovsky, Anouck Girard, H. Eric Tseng, Dimitar Filev

This paper introduces the Generalized Action Governor, which is a supervisory scheme for augmenting a nominal closed-loop system with the capability of strictly handling constraints.

reinforcement-learning Reinforcement Learning (RL)

Safe and Human-Like Autonomous Driving: A Predictor-Corrector Potential Game Approach

no code implementations4 Aug 2022 Mushuang Liu, H. Eric Tseng, Dimitar Filev, Anouck Girard, Ilya Kolmanovsky

To address the challenges caused by the complexity in solving a multi-player game and by the requirement of real-time operation, a potential game (PG) based decision-making framework is developed.

Autonomous Driving Decision Making

Robust Action Governor for Uncertain Piecewise Affine Systems with Non-convex Constraints and Safe Reinforcement Learning

no code implementations17 Jul 2022 Yutong Li, Nan Li, H. Eric Tseng, Anouck Girard, Dimitar Filev, Ilya Kolmanovsky

The action governor is an add-on scheme to a nominal control loop that monitors and adjusts the control actions to enforce safety specifications expressed as pointwise-in-time state and control constraints.

Reinforcement Learning (RL) Safe Reinforcement Learning

Safe, Learning-Based MPC for Highway Driving under Lane-Change Uncertainty: A Distributionally Robust Approach

no code implementations27 Jun 2022 Mathijs Schuurmans, Alexander Katriniok, Christopher Meissen, H. Eric Tseng, Panagiotis Patrinos

We present a case study applying learning-based distributionally robust model predictive control to highway motion planning under stochastic uncertainty of the lane change behavior of surrounding road users.

Model Predictive Control Motion Planning

Overtaking Maneuvers on a Nonplanar Racetrack

1 code implementation22 Apr 2022 Thomas Fork, H. Eric Tseng, Francesco Borrelli

We leverage game theory and a new vehicle modeling approach to compute overtaking maneuvers for racecars on a nonplanar surface.

Vehicle Models and Optimal Control on a Nonplanar Surface

1 code implementation20 Apr 2022 Thomas Fork, H. Eric Tseng, Francesco Borrelli

We present a 10 DoF dynamic vehicle model for model-based control on nonplanar road surfaces.

Potential Game-Based Decision-Making for Autonomous Driving

no code implementations16 Jan 2022 Mushuang Liu, Ilya Kolmanovsky, H. Eric Tseng, Suzhou Huang, Dimitar Filev, Anouck Girard

Statistical comparative studies, including 1) finite potential game vs. continuous potential game, and 2) best response dynamics vs. potential function optimization, are conducted to compare the performances of different solution algorithms.

Autonomous Driving Decision Making

Interaction-Aware Trajectory Prediction and Planning for Autonomous Vehicles in Forced Merge Scenarios

no code implementations14 Dec 2021 Kaiwen Liu, Nan Li, H. Eric Tseng, Ilya Kolmanovsky, Anouck Girard

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 vehicles to identify or create a gap and safely merge into.

Autonomous Vehicles Model Predictive Control +1

Stochastic MPC with Multi-modal Predictions for Traffic Intersections

no code implementations20 Sep 2021 Siddharth H. Nair, Vijay Govindarajan, Theresa Lin, Chris Meissen, H. Eric Tseng, Francesco Borrelli

The use of feedback policies for prediction is motivated by the need for reduced conservatism in handling multi-modal predictions of the surrounding vehicles, especially prevalent in traffic intersection scenarios.

Autonomous Driving Collision Avoidance +1

Improved Robustness and Safety for Pre-Adaptation of Meta Reinforcement Learning with Prior Regularization

no code implementations19 Aug 2021 Lu Wen, Songan Zhang, H. Eric Tseng, Baljeet Singh, Dimitar Filev, Huei Peng

The performance of PEARL$^+$ is validated by solving three safety-critical problems related to robots and AVs, including two MuJoCo benchmark problems.

Autonomous Vehicles Decision Making +1

Quick Learner Automated Vehicle Adapting its Roadmanship to Varying Traffic Cultures with Meta Reinforcement Learning

1 code implementation18 Apr 2021 Songan Zhang, Lu Wen, Huei Peng, H. Eric Tseng

It is essential for an automated vehicle in the field to perform discretionary lane changes with appropriate roadmanship - driving safely and efficiently without annoying or endangering other road users - under a wide range of traffic cultures and driving conditions.

Meta Reinforcement Learning reinforcement-learning +1

Safe Reinforcement Learning Using Robust Action Governor

no code implementations21 Feb 2021 Yutong Li, Nan Li, H. Eric Tseng, Anouck Girard, Dimitar Filev, Ilya Kolmanovsky

Reinforcement Learning (RL) is essentially a trial-and-error learning procedure which may cause unsafe behavior during the exploration-and-exploitation process.

reinforcement-learning Reinforcement Learning (RL) +1

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