Han Su

Autonomous driving is an emerging technology that has developed rapidly over the last decade, with decision-making remaining a critical challenge, particularly due to its significant role in traffic congestion. In this paper, we propose a novel perception-and-decision framework, called HEAD, which consists of an enhanced perception module and a maneuver decision module to address this challenge. In the enhanced perception module, a graph-based state prediction model with a strategy of phantom vehicle construction is proposed to address incomplete vehicle features and predict future states in parallel. Then in the maneuver decision module, a deep reinforcement learning-based model is designed to learn a driving policy based on a parameterized action Markov decision process. A hybrid reward function takes into account aspects of safety, efficiency, comfort, and impact to guide the autonomous vehicle to make optimal maneuver decisions. To make our framework applicable to more scenarios, we further propose an improved HEAD (HEAD++) framework that makes the autonomous vehicle adapt to various road structures, such as lane merging and diverging scenarios. Besides, we develop a style tuning module in HEAD++, which supports personalized driving style tuning. To mitigate high training costs, an efficient style tuning method with approximate gradient descent is proposed to reduce the number of training iterations. Extensive experiments demonstrate the effectiveness of our framework. Compared to state-of-the-art methods, HEAD++ reduces overall traffic disturbance by 23.3%–40.9%, lowers collision risk by 4.5%–17.8%, and improves passenger comfort by 13.1%–30.5%, while maintaining high traffic efficiency.

Velocity control in autonomous driving is an emerging technology that has achieved rapid progress over the last decade. However, existing velocity control studies ignore cascading disturbances in multi-lane scenarios and usually ignore the negative impact caused by harsh velocity decisions. To address these issues, we propose a reinforcement learning-based framework, called RISE (contRol velocIty for autonomouS vEhicle) to make velocity decisions for an autonomous vehicle in multi-lane traffic scenarios. To detect latent disturbances in the traffic flow, we propose a novel state encoder to learn the spatio-temporal correlation between different vehicles based on a well-designed impact graph. Afterward, we introduce an actor-critic paradigm to make velocity decisions with the aid of a hybrid reward function considering four optimization objectives: safety, efficiency, comfort, and impact. In particular, the impact term can penalize the harsh decisions of the autonomous vehicle, thus encouraging it to reduce the negative impacts on traffic flow. Further, we propose an improved RISE (RISE++) framework that incorporates a motion prediction model to augment state features for reasonable decisions, a modification of the reward function for energy efficiency, and a multi-worker paradigm for training efficiency. Extensive experiments offer evidence that the proposed framework can advance the state of the art in terms of effectiveness and efficiency.

Autonomous driving is an emerging technology that is expected to bring significant social, economic, and environmental benefits. However, these benefits come with rising energy consumption by computation engines, limiting the driving range of vehicles, especially electric ones. Perception computing is typically the most power-intensive component, as it relies on largescale deep learning models to extract environmental features. Recently, numerous studies have employed model compression techniques, such as sparsification, quantization, and distillation, to reduce computational consumption. However, these methods often result in either a substantial model size or a significant drop in perception accuracy compared to high-computation models. To address these challenges, we propose an energy-efficient autonomous driving framework, called EneAD. In the adaptive perception module, a perception optimization strategy is designed from the perspective of data management and tuning. Firstly, we manage multiple perception models with different computational consumption and adjust the execution framerate dynamically. Then, we define them as knobs and design a transferable tuning method based on Bayesian optimization to identify promising knob values that achieve low computation while maintaining desired accuracy. To adaptively switch the knob values in various traffic scenarios, a lightweight classification model is proposed to distinguish the perception difficulty in different scenarios. In the robust decision module, we propose a decision model based on reinforcement learning and design a regularization term to enhance driving stability in the face of perturbed perception results. Extensive experiments evidence the superiority of our framework in both energy consumption and driving performance. EneAD can reduce perception consumption by 1.9x to 3.5x and thus improve driving range by 3.9% to 8.5%

In the realm of big data and cloud analytics, efficiently managing and retrieving high-dimensional data presents a critical challenge. Traditional indexes often struggle with the storage overhead inherent in large datasets. There is a growing interest in the adoption of Small Materialize Aggregation (SMA) among cloud database vendors due to its ability to maintain lightweight block-level metadata, facilitating efficient block skipping. However, SMA performance relies heavily on data layout. This is especially critical in scenarios with wide tables containing hundreds of dimensions, where the curse of dimensionality exacerbates the issue. In this paper, we propose AdaCurve , a novel approach aimed at enhancing block skipping in high-dimensional datasets through adaptive optimization of data layout. Unlike conventional static and non-adaptive space-filling curves (SFCs), AdaCurve leverages machine learning to develop an adaptive curve---a dynamically adjusting optimal projection function tailored to high-dimensional workloads and data characteristics. We introduce an attention-based network to handle high-dimensional data and a learnable objective for training adaptive curves in an end-to-end manner. Extensive experiments conducted on the Spark with real-world datasets demonstrate the effectiveness of AdaCurve . We have shown that AdaCurve effectively scales to datasets with dimensions of up to 1,000 columns, achieving a 2.8× improvement in block skipping compared to SFCs.

The performance of modern database management systems highly relies on hundreds of adjustable knobs. Traditionally, these knobs are manually adjusted by database administrators, a process that is both inefficient and ineffective for tuning large-scale databases in cloud environments. Recent research has explored the use of machine learning techniques to enable the automatic tuning of database configurations. Although most existing learning-based methods achieve satisfactory results on static workloads, they often experience performance degradation and low sampling efficiency in real-world environments. According to our study, this is primarily due to a lack of safety guarantees during the configuration sampling process. To address the aforementioned issues, we propose SafeTune, an online tuning system that adapts to dynamic workloads. Our core idea is to filter out a large number of configurations with potential risks during the configuration sampling process. We employ a two-stage filtering approach: The first stage utilizes a semi-supervised outlier ensemble with feature learning to achieve high-quality feature representation. The second stage employs a ranking-based classifier to refine the filtering process. In addition, to alleviate the cold-start problem, we leverage the historical tuning experience to provide high-quality initial samples during the initialization phase. We conducted comprehensive evaluations on static and dynamic workloads. In comparison to offline baseline methods, SafeTune reduces 95.6%-98.6% unsafe configuration suggestions. In contrast with state-of-the-art methods, SafeTune has improved cumulative performance by 10.5%-46.6% and tuning speed by 15.1%-35.4%.

Automation in road vehicles is an emerging technology that has developed rapidly over the last decade. There have been many inter-disciplinary challenges posed on existing transportation infrastructure by autonomous vehicles. In this paper, we conduct an algorithmic study on when and how an autonomous vehicle should change its lane, which is a fundamental problem in vehicle automation field and root cause of most ‘phantom’ traffic jams. We propose a prediction-and-decision framework, called Cheetah ( Ch ang e lan e smar t for a utonomous ve h icle), which aims to optimize the lane changing maneuvers of autonomous vehicle while minimizing its impact on surrounding vehicles. In the prediction phase, Cheetah learns the spatio-temporal dynamics from historical trajectories of surrounding vehicles with a deep model (GAS-LED model) and predict their corresponding actions in the near future. A global attention mechanism and state sharing strategy are also incorporated to achieve higher accuracy and better convergence efficiency. Then in the decision phase, Cheetah looks for optimal lane change maneuvers for the autonomous vehicle by taking into account a few factors such as speed, impact on other vehicles and safety issues. A tree-based adaptive beam search algorithm is designed to reduce the search space and improve accuracy. In order to make our framework applicable to more scenarios, we further propose an improved Cheetah (Cheetah ⁺ ) framework that makes the autonomous vehicle adapt for exiting a road and meet the requirement for driving comfort. Extensive experiments offer evidence that the proposed framework can advance the state of the art in terms of effectiveness and efficiency.