Search Results for author: Ching-Yao Chan

Found 16 papers, 0 papers with code

A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles

no code implementations29 May 2021 Fei Ye, Shen Zhang, Pin Wang, Ching-Yao Chan

In this survey, we systematically summarize the current literature on studies that apply reinforcement learning (RL) to the motion planning and control of autonomous vehicles.

Autonomous Driving Motion Planning +1

Meta-Adversarial Inverse Reinforcement Learning for Decision-making Tasks

no code implementations23 Mar 2021 Pin Wang, Hanhan Li, Ching-Yao Chan

Therefore, it is desirable for the trained model to adapt to new tasks that have limited data samples available.

Decision Making Imitation Learning +2

Meta Reinforcement Learning-Based Lane Change Strategy for Autonomous Vehicles

no code implementations28 Aug 2020 Fei Ye, Pin Wang, Ching-Yao Chan, Jiucai Zhang

The simulation results shows that the proposed method achieves an overall success rate up to 20% higher than the benchmark model when it is generalized to the new environment of heavy traffic density.

Autonomous Vehicles Imitation Learning +2

Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning

no code implementations7 Feb 2020 Fei Ye, Xuxin Cheng, Pin Wang, Ching-Yao Chan, Jiucai Zhang

The simulation results demonstrate the lane change maneuvers can be efficiently learned and executed in a safe, smooth, and efficient manner.

Autonomous Driving reinforcement-learning

TPPO: A Novel Trajectory Predictor with Pseudo Oracle

no code implementations4 Feb 2020 Biao Yang, Caizhen He, Pin Wang, Ching-Yao Chan, Xiaofeng Liu, Yang Chen

A latent variable predictor is proposed to estimate latent variable distributions from observed and ground-truth trajectories.

Autonomous Driving Human-Object Interaction Detection

A Novel Graph based Trajectory Predictor with Pseudo Oracle

no code implementations2 Feb 2020 Biao Yang, Guocheng Yan, Pin Wang, Ching-Yao Chan, Xiang Song, Yang Chen

Recent studies focus on modeling pedestrians' motion patterns with recurrent neural networks, capturing social interactions with pooling-based or graph-based methods, and handling future uncertainties by using random Gaussian noise as the latent variable.

Graph Attention Pedestrian Trajectory Prediction +2

Quadratic Q-network for Learning Continuous Control for Autonomous Vehicles

no code implementations29 Nov 2019 Pin Wang, Hanhan Li, Ching-Yao Chan

Reinforcement Learning algorithms have recently been proposed to learn time-sequential control policies in the field of autonomous driving.

Autonomous Driving Continuous Control +2

Decision Making for Autonomous Driving via Augmented Adversarial Inverse Reinforcement Learning

no code implementations19 Nov 2019 Pin Wang, Dapeng Liu, Jiayu Chen, Hanhan Li, Ching-Yao Chan

Simulation results show that the augmented AIRL outperforms all the baseline methods, and its performance is comparable with that of the experts on all of the four metrics.

Autonomous Driving Decision Making +2

Intention-aware Long Horizon Trajectory Prediction of Surrounding Vehicles using Dual LSTM Networks

no code implementations6 Jun 2019 Long Xin, Pin Wang, Ching-Yao Chan, Jianyu Chen, Shengbo Eben Li, Bo Cheng

As autonomous vehicles (AVs) need to interact with other road users, it is of importance to comprehensively understand the dynamic traffic environment, especially the future possible trajectories of surrounding vehicles.

Autonomous Vehicles feature selection +2

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

Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning

no code implementations23 Apr 2019 Tianyu Shi, Pin Wang, Xuxin Cheng, Ching-Yao Chan, Ding Huang

We apply Deep Q-network (DQN) with the consideration of safety during the task for deciding whether to conduct the maneuver.

Autonomous Driving Decision Making +2

A Data Driven Method of Optimizing Feedforward Compensator for Autonomous Vehicle

no code implementations31 Jan 2019 Tianyu Shi, Pin Wang, Ching-Yao Chan, Chonghao Zou

A reliable controller is critical and essential for the execution of safe and smooth maneuvers of an autonomous vehicle. The controller must be robust to external disturbances, such as road surface, weather, and wind conditions, and so on. It also needs to deal with the internal parametric variations of vehicle sub-systems, including power-train efficiency, measurement errors, time delay, so on. Moreover, as in most production vehicles, the low-control commands for the engine, brake, and steering systems are delivered through separate electronic control units. These aforementioned factors introduce opaque and ineffectiveness issues in controller performance. In this paper, we design a feed-forward compensate process via a data-driven method to model and further optimize the controller performance. We apply the principal component analysis to the extraction of most influential features. Subsequently, we adopt a time delay neural network and include the accuracy of the predicted error in a future time horizon. Utilizing the predicted error, we then design a feed-forward compensate process to improve the control performance. Finally, we demonstrate the effectiveness of the proposed feed-forward compensate process in simulation scenarios.

Autonomous Ramp Merge Maneuver Based on Reinforcement Learning with Continuous Action Space

no code implementations25 Mar 2018 Pin Wang, Ching-Yao Chan

Most importantly, in contrast to most reinforcement learning applications in which the action space is resolved as discrete, our approach treats the action space as well as the state space as continuous without incurring additional computational costs.


Formulation of Deep Reinforcement Learning Architecture Toward Autonomous Driving for On-Ramp Merge

no code implementations7 Sep 2017 Pin Wang, Ching-Yao Chan

To tackle such a complicated control problem, we propose to apply Deep Reinforcement Learning (DRL) techniques for finding an optimal driving policy by maximizing the long-term reward in an interactive environment.

Autonomous Driving reinforcement-learning

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