Accurate and robust recognition and prediction of traffic situation plays an important role in autonomous driving, which is a prerequisite for risk assessment and effective decision making. Although there exist a lot of works dealing with modeling driver behavior of a single object, it remains a challenge to make predictions for multiple highly interactive agents that react to each other simultaneously. In this work, we propose a generic probabilistic hierarchical recognition and prediction framework which employs a two-layer Hidden Markov Model (TLHMM) to obtain the distribution of potential situations and a learning-based dynamic scene evolution model to sample a group of future trajectories. Instead of predicting motions of a single entity, we propose to get the joint distribution by modeling multiple interactive agents as a whole system. Moreover, due to the decoupling property of the layered structure, our model is suitable for knowledge transfer from simulation to real world applications as well as among different traffic scenarios, which can reduce the computational efforts of training and the demand for a large data amount. A case study of highway ramp merging scenario is demonstrated to verify the effectiveness and accuracy of the proposed framework.
The objective of transfer learning is to enhance the learning efficiency and performance of the target tasks through taking advantages of the knowledge learned from the source tasks @cite_4 . In @cite_16 , the authors proposed a transfer learning method to recognize daily activities of different-aged people in different house settings. A mapping across various sensor network raw data was proposed to extract common meta features among diverse tasks. In @cite_8 , put forward a transfer learning approach where a navigation map learned from the training data and a dynamic Bayesian network are combined to predict traffic participants' motion patterns in novel scenes that are not included in the training phase. In this paper, our proposed model enables simulation to real world knowledge transfer, which makes up the difficulties of collecting real driving data.
@cite_16 @cite_4 @cite_8
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Transfer learning aims at reusing the knowledge in some source tasks to improve the learning of a target task. Many transfer learning methods assume that the source tasks and the target task be related, even though many tasks are not related in reality. However, when two tasks are unrelated, the knowledge extracted from a source task may not help, and even hurt, the performance of a target task. Thus, how to avoid negative transfer and then ensure a "safe transfer" of knowledge is crucial in transfer learning. In this paper, we propose an Adaptive Transfer learning algorithm based on Gaussian Processes (AT-GP), which can be used to adapt the transfer learning schemes by automatically estimating the similarity between a source and a target task. The main contribution of our work is that we propose a new semi-parametric transfer kernel for transfer learning from a Bayesian perspective, and propose to learn the model with respect to the target task, rather than all tasks as in multi-task learning. We can formulate the transfer learning problem as a unified Gaussian Process (GP) model. The adaptive transfer ability of our approach is verified on both synthetic and real-world datasets. Transfer learning borrows knowledge from a source domain to facilitate learning in a target domain. Two primary issues to be addressed in transfer learning are what and how to transfer. For a pair of domains, adopting different transfer learning algorithms results in different knowledge transferred between them. To discover the optimal transfer learning algorithm that maximally improves the learning performance in the target domain, researchers have to exhaustively explore all existing transfer learning algorithms, which is computationally intractable. As a trade-off, a sub-optimal algorithm is selected, which requires considerable expertise in an ad-hoc way. Meanwhile, it is widely accepted in educational psychology that human beings improve transfer learning skills of deciding what to transfer through meta-cognitive reflection on inductive transfer learning practices. Motivated by this, we propose a novel transfer learning framework known as Learning to Transfer (L2T) to automatically determine what and how to transfer are the best by leveraging previous transfer learning experiences. We establish the L2T framework in two stages: 1) we first learn a reflection function encrypting transfer learning skills from experiences; and 2) we infer what and how to transfer for a newly arrived pair of domains by optimizing the reflection function. Extensive experiments demonstrate the L2T's superiority over several state-of-the-art transfer learning algorithms and its effectiveness on discovering more transferable knowledge. In a reinforcement learning setting, the goal of transfer learning is to improve performance on a target task by re-using knowledge from one or more source tasks. A key problem in transfer learning is how to choose appropriate source tasks for a given target task. Current approaches typically require that the agent has some experience in the target domain, or that the target task is specified by a model (e.g., a Markov Decision Process) with known parameters. To address these limitations, this paper proposes a framework for selecting source tasks in the absence of a known model or target task samples. Instead, our approach uses meta-data (e.g., attribute-value pairs) associated with each task to learn the expected benefit of transfer given a source-target task pair. To test the method, we conducted a large-scale experiment in the Ms. Pac-Man domain in which an agent played over 170 million games spanning 192 variations of the task. The agent used vast amounts of experience about transfer learning in the domain to model the benefit (or detriment) of transferring knowledge from one task to another. Subsequently, the agent successfully selected appropriate source tasks for previously unseen target tasks. When transferring knowledge between reinforcement learning agents with different state representations or actions, past knowledge must be efficiently mapped to novel tasks so that it aids learning. The majority of the existing approaches use pre-defined mappings provided by a domain expert. To overcome this limitation and enable autonomous transfer learning, this paper introduces a method for weighting and using multiple inter-task mappings based on a probabilistic framework. Experimental results show that the use of multiple inter-task mappings, accompanied with a probabilistic selection mechanism, can significantly boost the performance of transfer learning relative to 1) learning without transfer and 2) using a single hand-picked mapping. We especially introduce novel tasks for transfer learning in a realistic simulation of the iCub robot, demonstrating the ability of the method to select mappings in complex tasks where human intuition could not be applied to select them. The results verified the efficacy of the proposed approach in a real world and complex environment.