It is recognized that the control of mixed-autonomy platoons comprising connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) can enhance traffic flow. Among existing methods, Multi-Agent Reinforcement Learning (MARL) appears to be a promising control strategy because it can manage complex scenarios in real time. However, current research on MARL-based mixed-autonomy platoon control suffers from several limitations. First, existing MARL approaches address safety by penalizing safety violations in the reward function, thus lacking theoretical safety guarantees due to the limited interpretability of RL. Second, few studies have explored the cooperative safety of multi-CAV platoons, where CAVs can be coordinated to further enhance the system-level safety involving the safety of both CAVs and HDVs. Third, existing work tends to make an unrealistic assumption that the behavior of HDVs and CAVs is publicly known and rational. To bridge the research gaps, we propose a safe MARL framework for mixed-autonomy platoons. Specifically, this framework 1) characterizes cooperative safety by designing a cooperative Control Barrier Function (CBF), enabling CAVs to collaboratively improve the safety of the entire platoon, 2) provides a safety guarantee to the MARL-based controller by integrating the CBF-based safety constraints into MARL through a differentiable quadratic programming (QP) layer, and 3) incorporates a conformal prediction module that enables each CAV to estimate the unknown behaviors of the surrounding vehicles with uncertainty qualification. Simulation results show that our proposed control strategy can effectively enhance the system-level safety through CAV cooperation of a mixed-autonomy platoon with a minimal impact on control performance.

Structure-based drug design (SBDD), aiming to generate 3D molecules with high binding affinity toward target proteins, is a vital approach in novel drug discovery. Although recent generative models have shown great potential, they suffer from unstable probability dynamics and mismatch between generated molecule size and the protein pockets geometry, resulting in inconsistent quality and off-target effects. We propose PAFlow, a novel target-aware molecular generation model featuring prior interaction guidance and a learnable atom number predictor. PAFlow adopts the efficient flow matching framework to model the generation process and constructs a new form of conditional flow matching for discrete atom types. A protein–ligand interaction predictor is incorporated to guide the vector field toward higher-affinity regions during generation, while an atom number predictor based on protein pocket information is designed to better align generated molecule size with target geometry. Extensive experiments on the CrossDocked2020 benchmark show that PAFlow achieves a new state-of-the-art in binding affinity (up to -8.31 Avg. Vina Score), simultaneously maintains favorable molecular properties.

Optimizing mixed vehicle platoons, which consist of connected and automated vehicles (CAVs) with human-driven vehicles (HDVs), is a critical challenge for intelligent transportation systems. While existing predictive control methods have improved modeling accuracy and control robustness, they are often constrained by their reliance on online optimization, limiting their applicability in real-time scenarios. To address this gap, this paper proposes a Robust Explicit Data-Driven Predictive Control (REDDPC) framework designed to provide robust and real-time control for mixed vehicle platoons. The framework begins by utilizing a deep Koopman operator network to learn the nonlinear dynamics of the system. Using this learned representation, the neural network-based control policy is then optimized through backpropagation, eliminating the need for online optimization. To enhance robustness, a reachability-based safety filter is integrated with the learned control policy to dynamically adjust control inputs, ensuring platoon safety under complex conditions. Simulation and experiment results demonstrate that the proposed method achieves superior tracking performance under noise, disturbance, and attack conditions, while significantly reducing online computational time, making it highly suitable for real-world deployment.

Structure-based drug design (SBDD) is a critical subtask in the drug discovery process, with deep generative models playing a pivotal role. Inherently, drug design is a multi-objective task given the fact that a promising drug candidate must satisfy multiple properties. However, existing SBDD methods either focus solely on the binding affinity between molecules and target proteins while neglecting other crucial properties, or they assume that objective properties are independent of each other. Yet there are often potential relationships among properties, which can be conflicting-improving one property may lead to the deterioration of another. The lack of consideration for these relationships in current methods makes it unfeasible to generate molecules that simultaneously meet multiple objectives. To address the above issues, a multi-objective SBDD algorithm is proposed based on the diffusion model to optimize binding affinity and other drug properties simultaneously. Multiple expert networks are trained in parallel to predict properties for molecules in intermediate states and transmit gradients, and a causal graph is constructed through the causal discovery algorithm to unveil the underlying relationships among target properties. During the entire generation process, the joint distribution of target properties is decomposed in a reasonable manner according to the casual graph, and then the gradients of each property are applied to guide the optimizing direction of generation. Experimental results indicate that our model effectively optimizes multiple objectives simultaneously, generating molecules with greater drug potential compared to baseline models.

A critical aspect of safe and efficient motion planning for autonomous vehicles (AVs) is to handle the complex and uncertain behavior of surrounding human-driven vehicles (HDVs). Despite intensive research on driver behavior prediction, existing approaches often overlook the interactions between AVs and HDVs, assuming that HDV trajectories are not influenced by AV actions. To address this gap, we present a transformer-transfer learning-based interaction-aware trajectory predictor for safe motion planning in autonomous driving, focusing on a vehicle-to-vehicle (V2V) interaction scenario involving an AV and an HDV. Specifically, we construct a transformer-based interaction-aware trajectory predictor using widely available datasets of HDV trajectory data and further transfer the learned predictor using a small set of AV-HDV interaction data. Then, to better incorporate the proposed trajectory predictor into the motion planning module of AVs, we introduce an uncertainty quantification method to characterize the predictor’s errors, which are integrated into the path-planning process. Our experimental results demonstrate the value of explicitly considering interactions and handling uncertainties.

Efficiency is critical for autonomous vehicles (AVs), especially emergency AVs. However, most existing methods focus on regular vehicles, overlooking the different strategies required by emergency vehicles to address the challenge of maximizing efficiency while ensuring safety. In this paper, we propose an Integrated Agile Decision-Making with Active and Safety-Critical Motion Planning System (IDEAM). IDEAM focuses on enabling emergency AVs, such as ambulances, to actively achieve efficiency in dense traffic scenarios with safety in mind. Firstly, the speed-centric decision-making algorithm named the long short-term spatio-temporal graph-centric decision-making (LSGM) is given. LSGM comprises conditional depth-first search (C-DFS) for multiple path generation as well as methods for speed gains and risk evaluation for path selection, which presents a robust algorithm for high efficiency and safety consideration. Secondly, with an output path from LSGM, the motion planner reconsiders environmental conditions to decide constraint states for the final planning stage, among which the lane-probing state is designed for actively attaining spatial and speed advantage. Thirdly, under the Frenet-based model predictive control (MPC) framework with final constraints state and selected path, the safety-critical motion planner employs decoupled discrete control barrier functions (DCBFs) and linearized discrete-time high-order control barrier functions (DHOCBFs) to model the constraints associated with different driving behaviors, making the optimization problem convex. Finally, we extensively validate our system using scenarios from a randomly synthetic dataset, which reveal that IDEAM improves average route progress by approximately 5.25% to 12.93% and increases average speed by about 4.5% to 9.8% compared to the benchmark method, demonstrating its capability to achieve speed benefits and assure safety simultaneously. Simulation video is available at: https://www.youtube.com/watch?v=873BZoQSf-Q. Our implementation code is available at https://github.com/YimingShu-teay/IDEAM.git

Generating high-quality and drug-like molecules from scratch within the expansive chemical space presents a significant challenge in the field of drug discovery. In prior research, value-based reinforcement learning algorithms have been employed to generate molecules with multiple desired properties iteratively. The immediate reward was defined as the evaluation of intermediate-state molecules at each step, and the learning objective would be maximizing the expected cumulative evaluation scores for all molecules along the generative path. However, this definition of the reward was misleading, as in reality, the optimization target should be the evaluation score of only the final generated molecule. Furthermore, in previous works, randomness was introduced into the decision-making process, enabling the generation of diverse molecules but no longer pursuing the maximum future rewards. In this paper, immediate reward is defined as the improvement achieved through the modification of the molecule to maximize the evaluation score of the final generated molecule exclusively. Originating from the A search, path consistency (PC), i.e., values on one optimal path should be identical, is employed as the objective function in the update of the value estimator to train a multi-objective de novo drug designer. By incorporating the value into the decision-making process of beam search, the DrugBA algorithm is proposed to enable the large-scale generation of molecules that exhibit both high quality and diversity. Experimental results demonstrate a substantial enhancement over the state-of-the-art algorithm QADD in multiple molecular properties of the generated molecules.

This paper proposes a comprehensive framework to enable autonomous vehicles (AVs) with the highest priority (e.g., ambulances) to perform lane change maneuvers timely and continuously to achieve speed benefits without compromising safety. This type of vehicles can override traffic rules and drive at high speeds when necessary. The proposed framework comprises three parts: a decision-making layer, a motion planning layer, and a safety filter for the controller. The discrete decisions are coordinated by a speed-oriented finite state machine (FSM). Once the decision has been made, model predictive control (MPC) is utilized for planning and control, ensuring safety guarantees. Safety filters are constructed as longitudinal and lateral constraints by decoupled and discrete control barrier functions (DCBFs), combined with MPC, making it a convex optimization problem. Specifically, the region of interest (ROI) is employed to determine the range of the activation of lateral constraints. The proposed framework is tested through comparative numerical simulations, demonstrating its ability to gain speed and ensure safety across randomly generated driving scenarios. Additionally, some emergency scenarios have been considered in the experiment.