Data-driven System Identification and Optimal Control Framework for Grand-Prix Style Autonomous Racing

For the past 30 years, autonomous driving has witnessed a tremendous improvements thanks to the surge of computing power. Not only did we witness the autonomous vehicle navigate itself safely in the urban area, stories about more diverse autonomous driving applications, such as off-road rally-style navigation, are also commonly mentioned. Just until recently, the exponential increase in GPU and high-performance computing technology has motivated the research on autonomous driving under extreme situations such as autonomous racing or drifting.[25] The motivation for this thesis is to offer a brief overview about the main challenge of autonomous driving control and planning in racing scenario along with the potential solutions. The first contribution is using koopmam operator and deep neural network to perform data-driven system identification. We then design optimal model-based control which is based on the learned dynamics alone. Based on our new system identification algorithm, we can approximate an accurate, explainable, and linearized system representation in a high-dimensional latent space, without any prior knowledge of the system. In this case, the learned vehicle dynamic automatically involves the information that is normally difficult to obtain, including cornering stiffness, tire slip, transmission parameters, etc. Our result shows that our koopman data-driven optimal control approach is able to deliver better tracking accuracy at high speed compared to the state-of-art vehicle controllers. The second contribution is an iterative learning and sampling algorithm that can perform minimum-time optimization of the global racing trajectory(aka racing line) within the limit of tire friction. This trajectory optimization algorithm is not only proven to be computationally efficient, but also safe enough for the onboard RC vehicle’s test. The research achievements we made for the last two years not only enables the F1TENTH racing team of Clemson University Mechanical Engineering Department to finish top 5 in both virtual autonomous racing hosted by IFAC and IROS congress, but also offer us the opportunity to join ICRA 2021 Autonomous racing workshop to present our work and being awarded the joint best paper. More importantly, these contributions proved to be functional and effective in the on-board testing of the real F1TENTH robot’s autonomous navigation in the Flour Danial basement. Finally, this thesis will also include discussions of the potential research directions that can help improve the our current method so that it can better contribute to the autonomous driving industry

汉语话语标记"所以"--母语者和新加坡汉语学习者使用情况对比研究 = The Chinese Discourse Marker "Suoyi": A Comparative Study of its Usage between Native Speakers and Singapore Mandarin Learners

Master'sMASTER OF ART

Modeling Multi-interest News Sequence for News Recommendation

A session-based news recommender system recommends the next news to a user by modeling the potential interests embedded in a sequence of news read/clicked by her/him in a session. Generally, a user's interests are diverse, namely there are multiple interests corresponding to different types of news, e.g., news of distinct topics, within a session. %Modeling such multiple interests is critical for precise news recommendation. However, most of existing methods typically overlook such important characteristic and thus fail to distinguish and model the potential multiple interests of a user, impeding accurate recommendation of the next piece of news. Therefore, this paper proposes multi-interest news sequence (MINS) model for news recommendation. In MINS, a news encoder based on self-attention is devised on learn an informative embedding for each piece of news, and then a novel parallel interest network is devised to extract the potential multiple interests embedded in the news sequence in preparation for the subsequent next-news recommendations. The experimental results on a real-world dataset demonstrate that our model can achieve better performance than the state-of-the-art compared models

Competition of Chiroptical Effect Caused by Nanostructure and Chiral Molecules

The theory to calculate circular dichroism (CD) of chiral molecules in a finite cluster with arbitrarily disposed objects has been developed by means of T-matrix method. The interactions between chiral molecules and nanostructures have been investigated. Our studies focus on the case of chiral molecules inserted into plasmonic hot spots of nanostructures. Our results show that the total CD of the system with two chiral molecules is not sum for two cases when two chiral molecules inserted respectively into the hot spots of nanoparticle clusters as the distances among nanoparticles are small, although the relationship is established at the case of large interparticle distances. The plasmonic CD arising from structure chirality of nanocomposites depends strongly on the relative positions and orientations of nanospheroids, and are much greater than that from molecule-induced chirality. However, the molecule-induced plasmonic CD effect from the molecule-NP nanocomposites with special chiral structures can be spectrally distinguishable from the structure chirality-based optical activity. Our results provide a new theoretical framework for understanding the two different aspects of plasmonic CD effect in molecule-NP nanocomposites, which would be helpful for the experimental design of novel biosensors to realize ultrasensitive probe of chiral information of molecules by plasmon-based nanotechnology