The preparation of intelligent-responsive materials with controllable topology structure has long been a significant objective for chemists in the field of materials science. In this paper, we ...designed and prepared a linear-cyclic reversible topological structure polymer based on the bistable 1rotaxane molecular shuttle. A ferrocene-functionalized 1rotaxane and naphthalimide fluorophore group are introduced into the both ends of the polymer, which exhibit distance-induced photo-electron transfer effect. The structural transformation between linear and cyclic state of polymer is demonstrated by simple acid-base stimuli, accompanying visual fluorescence changes. The transformation process was characterized by 1H NMR spectra and fluorescence spectra. This work provides a novel strategy to construct functionalized polymers with topological structure.
Here, we designed and prepared a linear-cyclic reversible topological structure polymer based on the bistable 1rotaxane molecular shuttle. A ferrocene-functionalized 1rotaxane and naphthalimide fluorophore group are introduced into the both ends of the polymer, which exhibit distance-induced photo-electron transfer effect. The structural transformation between linear and cyclic state of polymer is demonstrated by simple acid-base stimuli, accompanying visual fluorescence changes. Display omitted
Herein, a novel pillar5arene-bridged monomer with two sides functionalized with tetraphenylethylene (TPE) and nitrile group was designed and synthesized. It could form supramolecular aggregates based ...on the host-guest interaction between TPE and the nitrile group. Due to the restricted intramolecular rotation of the TPE core, the supramolecular aggregates showed prominent aggregation-induced emission.
Supramolecular aggregates with aggregation-induced emission were constructed by pillar5arene-based host-guest interaction.
Vehicle and pedestrian detection is one of the critical tasks in autonomous driving. Since heterogeneous techniques have been proposed, the selection of a detection system with an appropriate balance ...among detection accuracy, speed and memory consumption for a specific task has become very challenging. To deal with this issue and to provide guidance for model selection, this paper analyzes several mainstream object detection architectures, including Faster R-CNN, R-FCN, and SSD, along with several typical feature extractors, such as ResNet50, ResNet101, MobileNet_V1, MobileNet_V2, Inception_V2 and Inception_ResNet_V2. By conducting extensive experiments using the KITTI benchmark, which is a commonly used street dataset, we demonstrate that Faster R-CNN ResNet50 obtains the best average precision (AP) (58%) for vehicle and pedestrian detection, with a speed of 8.6 FPS. Faster R-CNN Inception_V2 performs best for detecting cars and detecting pedestrians respectively (74.5% and 47.3%). ResNet101 consumes the highest memory (9907 MB) and has the largest number of parameters (64.42 millions), and Inception_ResNet_V2 is the slowest model (3.05 FPS). SSD MobileNet_V2 is the fastest model (70 FPS), and SSD MobileNet_V1 is the lightest model in terms of memory usage (875 MB), both of which are suitable for applications on mobile and embedded devices.
Motion prediction is the fundamental input for decision-making in autonomous vehicles. The current motion prediction solutions are designed with a strong reliance on black box predictions based on ...neural networks (NNs), which is unacceptable for safety-critical applications. Motion prediction with high uncertainty can cause conflicting decisions and even catastrophic results. To address this issue, an uncertainty estimation approach based on the deep ensemble technique is proposed for motion prediction in this paper. Subsequently, the estimated uncertainty is considered in the decision-making module to improve driving safety. Firstly, a motion prediction model based on long short-term memory (LSTM) is built and the deep ensemble technique is utilized to obtain both epistemic and aleatoric uncertainty of the prediction model. Besides, an uncertainty-aware potential field is developed to process the prediction uncertainty. Furthermore, a decision-making framework is proposed based on the model predictive control algorithm that considers the uncertainty-aware potential field, road boundaries, and multiple constraints of vehicle dynamics. Finally, the public available NGSIM , HighD and INTERACTION datasets are used to evaluate the proposed motion prediction model. More importantly, two traffic scenarios are also extracted from NGSIM and INTERACTION datasets to verify the effectiveness of the proposed decision-making method and in particular, its real-time performance is shown by employing a hardware-in-the-loop (HiL) experiment bench.
Accurate and fast 3D object detection from point clouds is a key task in autonomous driving. Existing one-stage 3D object detection methods can achieve real-time performance, however, they are ...dominated by anchor-based detectors which are inefficient and require additional post-processing. In this paper, we eliminate anchors and model an object as a single point-the center point of its bounding box. Based on the center point, we propose an anchor-free CenterNet3D network that performs 3D object detection without anchors. Our CenterNet3D uses keypoint estimation to find center points and directly regresses 3D bounding boxes. However, because inherent sparsity of point clouds, 3D object center points are likely to be in empty space which makes it difficult to estimate accurate boundaries. To solve this issue, we propose an extra corner attention module to enforce the CNN backbone to pay more attention to object boundaries. Besides, considering that one-stage detectors suffer from the discordance between the predicted bounding boxes and corresponding classification confidences, we develop an efficient keypoint-sensitive warping operation to align the confidences to the predicted bounding boxes. Our proposed CenterNet3D is non-maximum suppression free which makes it more efficient and simpler. We evaluate CenterNet3D on the widely used KITTI dataset and more challenging nuScenes dataset. Our method outperforms all state-of-the-art anchor-based one-stage methods and has comparable performance to two-stage methods as well. It has an inference speed of 20 FPS and achieves the best speed and accuracy trade-off. Our source code will be released at https://github.com/wangguojun2018/CenterNet3d .
To improve the safety and driving stability of the autonomous heavy truck, it is necessary to consider the differences of driving behavior and drivable trajectories between the heavy trucks and ...passenger cars. This study proposes a probabilistic decision-making and trajectory planning framework for the autonomous heavy trucks. Firstly, the driving decision process is divided into intention generation and feasibility evaluations, which are realized using the utility theory and risk assessment, respectively. Subsequently the driving decision is made and sent to the trajectory planning module. In order to reflect the greater risks of the truck to other surrounding vehicles, the aggressiveness index (AI) is proposed and quantified to infer the asymmetrical risk level of lane-change maneuver. In the planning stage, the lateral and roll dynamics stability domains are developed as the constraints to exclude the candidate trajectories that would cause vehicle instability. Finally, the simulation results are compared between the proposed model and the artificial potential filed model in the scenarios extracted from the naturalistic driving data. It is shown that the proposed framework can provide the human-like lane-change decisions and truck-friendly trajectories, and performs well in dynamic driving environments.
Recently, thanks to the introduction of human feedback, Chat Generative Pre-trained Transformer (ChatGPT) has achieved remarkable success in the language processing field. Analogically, human drivers ...are expected to have great potential in improving the performance of autonomous driving under real-world traffic. Therefore, this study proposes a novel framework for evolutionary decision-making and planning by developing a hybrid augmented intelligence (HAI) method to introduce human feedback into the learning process. In the framework, a decision-making scheme based on interactive reinforcement learning (Int-RL) is first developed. Specifically, a human driver evaluates the learning level of the ego vehicle in real-time and intervenes to assist the learning of the vehicle with a conditional sampling mechanism, which encourages the vehicle to pursue human preferences and punishes the bad experience of conflicts with the human. Then, the longitudinal and lateral motion planning tasks are performed utilizing model predictive control (MPC), respectively. The multiple constraints from the vehicle's physical limitation and driving task requirements are elaborated. Finally, a safety guarantee mechanism is proposed to ensure the safety of the HAI system. Specifically, a safe driving envelope is established, and a safe exploration/exploitation logic based on the trial-and-error on the desired decision is designed. Simulation with a high-fidelity vehicle model is conducted, and results show the proposed framework can realize an efficient, reliable, and safe evolution to pursue higher traffic efficiency of the ego vehicle in both multi-lane and congested ramp scenarios.
In intelligent vehicle cooperative systems, the mismatch in driving characteristics between a human and a machine and the driver misoperation caused by this mismatch result in human-machine ...conflicts, which significantly affect driving safety. Therefore, an intelligent vehicle human-machine cooperative steering torque control method is proposed herein. To adapt the intelligent system to the varying previewing characteristics of a human, a time-varying previewing driver model is constructed, and a penalty factor for human-machine intervention is designed based on fuzzy rules to assign driving control rights by assessing the driver's state. Consequently, a human-vehicle-road model with driver preview time and penalty factor as varying parameters is established. Based on gain-scheduling control, a human-machine cooperative steering torque controller is designed to adapt to the varying previewing characteristics of a human and the change in human-machine intervention. The stability and robustness of the entire parameter space are guaranteed by constraining the poles in a certain region. Finally, the proposed human-machine cooperative control scheme demonstrates the effective alleviation of conflicts between the driver and the intelligent driving system.
To meet the urgent requirement of reliable artificial intelligence applications, we discuss the tight link between artificial intelligence and intelligence test in this paper. We highlight the role ...of tasks in intelligence test for all kinds of artificial intelligence. We explain the necessity and difficulty of describing tasks for intelligence test, checking all the tasks that may encounter in intelligence test, designing simulation-based test, and setting appropriate test performance evaluation indices. As an example, we present how to design reliable intelligence test for intelligent vehicles. Finally, we discuss the future research directions of intelligence test.
This article presents a framework to determine driving style and design a driver steering model considering driver characteristics. First, principal component analysis (PCA) and <inline-formula> ...<tex-math notation="LaTeX">K </tex-math></inline-formula>-means clustering are utilized to classify 30 participants into cautious, moderate, and aggressive drivers. Subsequently, a generic steering model is established based on the model predictive control method. Thereafter, the maximum lateral acceleration is extracted as a crucial indicator to represent driver characteristics, and it is calibrated through probabilistic models using the dataset, which consists of the classified drivers. Besides, point estimation model and interval estimation model are leveraged to determine driving style and adjust constraints in the stochastic programming-based steering model. Finally, simulation experiments present the variations of actual output trajectories between the aggressive drivers and the cautious drivers.
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