A Logic that Captures β\betaP on Ordered Structures

We extend the inflationary fixed-point logic, IFP, with a new kind of second-order quantifiers which have (poly-)logarithmic bounds. We prove that on ordered structures the new logic $\exists^{\log^{\omega}}\text{IFP}$ captures the limited nondeterminism class $\beta\text{P}$. In order to study its expressive power, we also design a new version of Ehrenfeucht-Fra\"iss\'e game for this logic and show that our capturing result will not hold on the general case, i.e. on all the finite structures.Comment: 15 pages. This article was reported with a title "Logarithmic-Bounded Second-Order Quantifiers and Limited Nondeterminism" in National Conference on Modern Logic 2019, on November 9 in Beijin

ProphNet: Efficient Agent-Centric Motion Forecasting with Anchor-Informed Proposals

Motion forecasting is a key module in an autonomous driving system. Due to the heterogeneous nature of multi-sourced input, multimodality in agent behavior, and low latency required by onboard deployment, this task is notoriously challenging. To cope with these difficulties, this paper proposes a novel agent-centric model with anchor-informed proposals for efficient multimodal motion prediction. We design a modality-agnostic strategy to concisely encode the complex input in a unified manner. We generate diverse proposals, fused with anchors bearing goal-oriented scene context, to induce multimodal prediction that covers a wide range of future trajectories. Our network architecture is highly uniform and succinct, leading to an efficient model amenable for real-world driving deployment. Experiments reveal that our agent-centric network compares favorably with the state-of-the-art methods in prediction accuracy, while achieving scene-centric level inference latency.Comment: CVPR 2023 (Highlight

Capturing the polynomial hierarchy by second-order revised Krom logic

We study the expressive power and complexity of second-order revised Krom logic (SO-KROM$^{r}$). On ordered finite structures, we show that its existential fragment $\Sigma^1_1$-KROM$^r$ equals $\Sigma^1_1$-KROM, and captures NL. On all finite structures, for $k\geq 1$, we show that $\Sigma^1_{k}$ equals $\Sigma^1_{k+1}$-KROM$^r$ if $k$ is even, and $\Pi^1_{k}$ equals $\Pi^1_{k+1}$-KROM$^r$ if $k$ is odd. The result gives an alternative logic to capture the polynomial hierarchy. We also introduce an extended version of second-order Krom logic (SO-EKROM). On ordered finite structures, we prove that SO-EKROM collapses to $\Pi^{1}_{2}$-EKROM and equals $\Pi^1_1$. Both of SO-EKROM and $\Pi^{1}_{2}$-EKROM capture co-NP on ordered finite structures

Capturing the polynomial hierarchy by second-order revised Krom logic

We study the expressive power and complexity of second-order revised Krom logic (SO-KROM$^{r}$). On ordered finite structures, we show that its existential fragment $\Sigma^1_1$-KROM$^r$ equals $\Sigma^1_1$-KROM, and captures NL. On all finite structures, for $k\geq 1$, we show that $\Sigma^1_{k}$ equals $\Sigma^1_{k+1}$-KROM$^r$ if $k$ is even, and $\Pi^1_{k}$ equals $\Pi^1_{k+1}$-KROM$^r$ if $k$ is odd. The result gives an alternative logic to capture the polynomial hierarchy. We also introduce an extended version of second-order Krom logic (SO-EKROM). On ordered finite structures, we prove that SO-EKROM collapses to $\Pi^{1}_{2}$-EKROM and equals $\Pi^1_1$. Both SO-EKROM and $\Pi^{1}_{2}$-EKROM capture co-NP on ordered finite structures

Driver Digital Twin for Online Prediction of Personalized Lane Change Behavior

Connected and automated vehicles (CAVs) are supposed to share the road with human-driven vehicles (HDVs) in a foreseeable future. Therefore, considering the mixed traffic environment is more pragmatic, as the well-planned operation of CAVs may be interrupted by HDVs. In the circumstance that human behaviors have significant impacts, CAVs need to understand HDV behaviors to make safe actions. In this study, we develop a Driver Digital Twin (DDT) for the online prediction of personalized lane change behavior, allowing CAVs to predict surrounding vehicles' behaviors with the help of the digital twin technology. DDT is deployed on a vehicle-edge-cloud architecture, where the cloud server models the driver behavior for each HDV based on the historical naturalistic driving data, while the edge server processes the real-time data from each driver with his/her digital twin on the cloud to predict the lane change maneuver. The proposed system is first evaluated on a human-in-the-loop co-simulation platform, and then in a field implementation with three passenger vehicles connected through the 4G/LTE cellular network. The lane change intention can be recognized in 6 seconds on average before the vehicle crosses the lane separation line, and the Mean Euclidean Distance between the predicted trajectory and GPS ground truth is 1.03 meters within a 4-second prediction window. Compared to the general model, using a personalized model can improve prediction accuracy by 27.8%. The demonstration video of the proposed system can be watched at https://youtu.be/5cbsabgIOdM

Innovative Machine Learning Methods for Demand Management in Smart Grid Market

Smart Grid has been widely acknowledged as an efficient solution to the current energy system. Smart Grid market is a complex and dynamic market with different types of consumers and suppliers under an uncertain environment. An efficient management of Smart Grid market can benefit Smart Grid in multiple aspects, including reducing energy cost, improving energy efficiency and enhancing network reliability. This thesis focuses on improving demand management in Smart Grid market through developing innovative machine learning methods

A hybrid-learning based broker model for strategic power trading in smart grid markets

Smart Grid markets are dynamic and complex, and brokers are widely introduced to better manage the markets. However, brokers face great challenges, including the varying energy demands of consumers, the changing prices in the markets, and the competitions between each other. This paper proposes an intelligent broker model based on hybrid learning (including unsupervised, supervised and reinforcement learning), which generates smart trading strategies to adapt to the dynamics and complexity of Smart Grid markets. The proposed broker model comprises three interconnected modules. Customer demand prediction module predicts short-term demands of various consumers with a data-driven method. Wholesale market module employs a Markov Decision Process for the one-day-ahead power auction based on the predicted demand. Retail market module introduces independent reinforcement learning processes to optimize prices for different types of consumers to compete with other brokers in the retail market. We evaluate the proposed broker model on Power TAC platform. The experimental results show that our broker is not only is competitive in making profit, but also maintains a good supply-demand balance. In addition, we also discover two empirical laws in the competitive power market environment, which are: 1. profit margin shrinks when there are fierce competitions in markets; 2. the imbalance rate of supply demand increases when the market environment is more competitive