Dynamic Target Research Articles

Safe, Efficient, and Comfortable Reinforcement-Learning-Based Car-Following for AVs with an Analytic Safety Guarantee and Dynamic Target Speed

Over the last decade, there has been rising interest in automated driving systems and adaptive cruise control (ACC). Controllers based on reinforcement learning (RL) are particularly promising for autonomous driving, being able to optimize a combination of criteria such as efficiency, stability, and comfort. However, RL-based controllers typically offer no safety guarantees. In this paper, we propose SECRM (the Safe, Efficient, and Comfortable RL-based car-following Model) for autonomous car-following that balances traffic efficiency maximization and jerk minimization, subject to a hard analytic safety constraint on acceleration. The acceleration constraint is derived from the criterion that the follower vehicle must have sufficient headway to be able to avoid a crash if the leader vehicle brakes suddenly. We critique safety criteria based on the time-to-collision (TTC) threshold (commonly used for RL controllers), and confirm in simulator experiments that a representative previous TTC-threshold-based RL autonomous-vehicle controller may crash (in both training and testing). In contrast, we verify that our controller SECRM is safe, in training scenarios with a wide range of leader behaviors, and in both regular-driving and emergency-braking test scenarios. We find that SECRM compares favorably in efficiency, comfort, and speed-following to both classical (non-learned) car-following controllers (intelligent driver model, Shladover, Gipps) and a representative RL-based car-following controller.

A Dynamics and Task Decoupled Reinforcement Learning Architecture for High-Efficiency Dynamic Target Intercept

A Dynamics and Task Decoupled Reinforcement Learning Architecture for High-Efficiency Dynamic Target Intercept

Multi-target two-photon dual-comb LiDAR.

By substituting two-photon cross-correlation in a wide-bandgap photodiode for the coherent gating conventionally used in dual-comb ranging, two-photon dual-comb LiDAR exchanges data-intensive interferometric acquisition for a single time-stamp from which an absolute distance can be inferred. Here, we report the application of two-photon dual-comb LiDAR to obtain real-time ranging to three independent targets with only a single silicon-photodiode detector. We show precisions of 197-255 nm (2 seconds averaging time) for static targets, and real-time simultaneous ranging to three dynamic targets driven by independent sinusoidal, saw-tooth and square waveforms. Finally, we demonstrate multi-target ranging to three points on a rigid body to provide simultaneous pitch and yaw angular measurements with precisions of 27.1 arcsec (130 µrad) on a 25 mm baseline.

Recent advances in acoustic technology for aquaculture: A review

AbstractAcoustic technology has great application prospects in aquaculture. In particular, two indispensable, critical technologies for the future aquaculture industry are multi‐sensor acquisition that can achieve multi‐scale information fusion, collection and establishment of a global acoustic fish database and highly developed deep learning intelligent algorithms that can establish a correlation mechanism between fish acoustic and behaviour characteristics. Acoustic technology offers remarkable advantages in large and turbid water bodies for studying spatial and temporal distribution patterns of aquatic organism populations, developing on‐demand feeding systems and estimating biomass. This article reviews the development of acoustic technology and its application in aquaculture over the last 30 years. It further analyses, in detail, the advantages and disadvantages of acoustic technology in evaluating aquatic organism biomass and morphological and physical indicators, aquatic organism behaviour and welfare improvement. Challenges of acoustic technology in acquiring dynamic target data accurately, building a global acoustic fish database and establishing connections between fish behaviour and acoustic characteristics are also discussed. In brief, this article aims to help researchers and practitioners better understand the current state‐of‐the‐art acoustic technologies, which can provide strong support for smart aquaculture applications.

Underwater Target Detection Algorithm Based on Feature Fusion Enhancement

Underwater robots that use optical images for dynamic target detection often encounter image blurring, poor contrast, and indistinct target features. As a result, the underwater robots have poor detection performance with a high rate of missed detections. To overcome these issues, a feature-enhanced algorithm for underwater target detection has been proposed in this paper. Based on YOLOv7, a feature enhancement module utilizing a triple-attention mechanism is developed to improve the network’s feature extraction ability without increasing the computational or algorithmic parameter quantity. Moreover, comprehensively considering the impact of a redundant feature in the images on detection accuracy, the ASPPCSPC structure was built. A parallel spatial convolutional pooling structure based on the original feature pyramid fusion structure, SPPCSPC, is introduced. The GhostNet network was utilized to optimize its convolution module, which reduces the model’s parameter quantity and optimizes the feature map. Furthermore, a Cat-BiFPN structure was designed to address the problem of fine-grained information loss in YOLOv7 feature fusion by adopting a weighted nonlinear fusion strategy to enhance the algorithm’s adaptability. Using the UPRC offshore dataset for validation, the algorithm’s detection accuracy was increased by 2.9%, and the recall rate was improved by 2.3% compared to the original YOLOv7 algorithm. In addition, the model quantity is reduced by 11.2%, and the model size is compressed by 10.9%. The experimental results significantly establish the validity of the proposed algorithm.

NIR-AIEgens nanospheres featuring high-fidelity dynamic lipid droplet targeting, expediting ferroptosis to annihilating tumor in hypoxia

Severe ferroptosis is a non-apoptotic iron-dependent cell death pathway, and extreme hypoxia and anomalous iron metabolism in tumors critically suppress the efficacy of ferroptosis therapy. Overcoming tumor hypoxia while facilitating ferroptosis is an extremely challenging task. Herein, the photosensitizer named BOTTQ with specific targeting of lipid droplet (LD) was synthesized by increasing the tail chain length of triphenylamine. The addition of alkoxy chains optimized the molecular structure and fostered inter-system crossing and type I reactive oxygen species (ROS) generation. Besides, BOTTQ yielded large amounts of type I ROS under light excitation oxidize efficiently polyunsaturated fatty acids in lipid monolayer of LD and contribute to the formation of ferroptosis substrate. Meanwhile, the generation of type I ROS accelerated the Fenton reaction, thus effectively boosting the occurrences of ferroptosis. Furthermore, the successful encapsulation of BOTTQ into DSPE-PEG-MAL material not only elevates the stable type of nanoparticle colloids, but the MAL moiety can deplete the functional thiols of glutathione in a mild environment, which leads to glutathione failure and facilitates ferroptosis. Thus tumor-specific ferroptosis-inducing strategy offers a universally applicable therapy with the capacity to enhance type I ROS production and effectively contain tumorigenesis.

Inertial Stable Platforms for Precision Pointing of Optical Systems in Aerospace Applications

Both airborne and space-based high-energy laser weapon systems present interesting challenges for precision line-of-sight control. Sub-microradian to microradian pointing accuracies are required against dynamic targets. In addition, “absolute” pointing and accurate inertial angular rate measurement are required to support mission requirements. This paper addresses these unique directed-energy weapon (DEW) precision-pointing requirements and their associated implementation alternatives in the context of strapdown and stable platform inertial reference technologies. The contributions of this paper consider the broader issues of DEW requirement drivers, integration of the stabilization system with the rest of the optical system, and design tradeoffs between stable platform and strapdown stabilization mechanizations. Both types of mechanisms are mounted to the gimbal of an imaging system using gryo sensors (gyros) and accelerometers to maintain a desired orientation in inertial space independent of the craft motion. Although both can include an optical reference that propagates through the optical path for optical alignment and jitter control, only in the stable platform is the optical reference stabilized. When combined on the same platform with a stabilized inertial measurement system and closed-loop stabilization with the optical reference, the mechanism becomes a stabilized inertial reference unit, which provides a stabilized optical reference to the electro-optical/laser pointing and jitter control systems.

Tracking of Multiple Static and Dynamic Targets for 4D Automotive Millimeter-Wave Radar Point Cloud in Urban Environments

This paper presents a target tracking algorithm based on 4D millimeter-wave radar point cloud information for autonomous driving applications, which addresses the limitations of traditional 2 + 1D radar systems by using higher resolution target point cloud information that enables more accurate motion state estimation and target contour information. The proposed algorithm includes several steps, starting with the estimation of the ego vehicle’s velocity information using the radial velocity information of the millimeter-wave radar point cloud. Different clustering suggestions are then obtained using a density-based clustering method, and correlation regions of the targets are obtained based on these clustering suggestions. The binary Bayesian filtering method is then used to determine whether the targets are dynamic or static targets based on their distribution characteristics. For dynamic targets, Kalman filtering is used to estimate and update the state of the target using trajectory and velocity information, while for static targets, the rolling ball method is used to estimate and update the shape contour boundary of the target. Unassociated measurements are estimated for the contour and initialized for the trajectory, and unassociated trajectory targets are selectively retained and deleted. The effectiveness of the proposed method is verified using real data. Overall, the proposed target tracking algorithm based on 4D millimeter-wave radar point cloud information has the potential to improve the accuracy and reliability of target tracking in autonomous driving applications, providing more comprehensive motion state and target contour information for better decision making.

Spatial-spectral resolution tunable snapshot imaging spectrometer: analytical design and implementation.

A snapshot imaging spectrometer is a powerful tool for dynamic target tracking and real-time recognition compared with a scanning imaging spectrometer. However, all the current snapshot spectral imaging techniques suffer from a major trade-off between the spatial and spectral resolutions. In this paper, an integral field snapshot imaging spectrometer (TIF-SIS) with a continuously tunable spatial-spectral resolution and light throughput is proposed and demonstrated. The proposed TIF-SIS is formed by a fore optics, a lenslet array, and a collimated dispersive subsystem. Theoretical analyses indicate that the spatial-spectral resolution and light throughput of the system can be continuously tuned through adjusting the F number of the fore optics, the rotation angle of the lenslet array, or the focal length of the collimating lens. Analytical relationships between the spatial and spectral resolutions and the first-order parameters of the system with different geometric arrangements of the lenslet unit are obtained. An experimental TIF-SIS consisting of a self-fabricated lenslet array with a pixelated scale of 100×100 and a fill factor of 0.716 is built. The experimental results show that the spectral resolution of the system can be steadily improved from 4.17 to 0.82nm with a data cube (N x×N y×N λ) continuously tuned from 35×35×36 to 40×40×183 in the visible wavelength range from 500 to 650nm, which is consistent with the theoretical prediction. The proposed method for real-time tuning of the spatial-spectral resolution and light throughput opens new possibilities for broader applications, especially for recognition of things with weak spectral signature and biomedical investigations where a high light throughput and tunable resolution are needed.

Dynamic Capture Using a Traplike Soft Gripper With Stiffness Anisotropy

Dynamic capture is a common skill that humans have practiced extensively but is a challenging task for robots in which sensing, planning, and actuation must be tightly coordinated to deal with targets of diverse shapes, sizes, and velocity. In particular, the impact force may cause serious damage to a rigid gripper and even its carrier, e.g., a robotic arm. Existing soft grippers suffer from low speed and force to actively respond to capturing dynamic targets. In this article, we propose a soft gripper capable of efficient capture of dynamic targets, taking inspiration from the biological structures of multitentacled animals or plants. The presented gripper uses a cluster of tentacles to achieve an omnidirectional envelope and high tolerance to dynamic target during the capturing process. In addition, a stiffness anisotropy property is implemented to the tentacle structure to form a “trap” making it easy for the targets to enter yet difficult to escape. We also present an analytical model for the tentacle structure to describe its deformation during the collision with a target. In experiments, we construct a robotic prototype and demonstrate its ability to capture dynamic targets.

Multi-UUV Maneuvering Counter-Game for Dynamic Target Scenario Based on Fractional-Order Recurrent Neural Network.

In this article, a multi-underwater unmanned vehicle (UUV) maneuvering decision-making algorithm is proposed for a counter-game with a dynamic target scenario. The game is modeled with interval-valued intuitionistic fuzzy rules, and an optimal maneuvering strategy is realized using a fractional-order recurrent neural network (RNN). First, underwater environments with weak connectivity, underwater noise, and dynamic uncertainties are analyzed and incorporated into the interval-valued intuitionistic fuzzy set. Then, the maneuvering decision-making model and the expected return of the multi-UUV countermeasure are designed based on the interval-valued intuitionistic fuzzy rules. Subsequently, to optimize the counter-game maneuvering strategy, a fractional-order RNN is formulated based on the Karush-Kuhn-Tucker optimality conditions. In addition, the existence and uniqueness of the optimal maneuvering solutions as well as the stability of the equilibrium point are discussed. Finally, simulation and experimental results are compared to determine the effectiveness of the proposed algorithm. The influence of the fractional order on the convergence rate and optimization error of the proposed algorithm is also minutely examined.

Fast prediction in marmoset reach-to-grasp movements for dynamic prey

Primates have evolved sophisticated, visually guided reaching behaviors for interacting with dynamic objects, such as insects, during foraging.1,2,3,4,5 Reaching control in dynamic natural conditions requires active prediction of the target's future position to compensate for visuo-motor processing delays and to enhance online movement adjustments.6,7,8,9,10,11,12 Past reaching research in non-human primates mainly focused on seated subjects engaged in repeated ballistic arm movements to either stationary targets or targets that instantaneously change position during the movement.13,14,15,16,17 However, those approaches impose task constraints that limit the natural dynamics of reaching. A recent field study in marmoset monkeys highlights predictive aspects of visually guided reaching during insect prey capture among wild marmoset monkeys.5 To examine the complementary dynamics of similar natural behavior within a laboratory context, we developed an ecologically motivated, unrestrained reach-to-grasp task involving live crickets. We used multiple high-speed video cameras to capture the movements of common marmosets (Callithrix jacchus) and crickets stereoscopically and applied machine vision algorithms for marker-free object and hand tracking. Contrary to estimates under traditional constrained reaching paradigms, we find that reaching for dynamic targets can operate at incredibly short visuo-motor delays around 80ms, rivaling the speeds that are typical of the oculomotor systems during closed-loop visual pursuit.18 Multivariate linear regression modeling of the kinematic relationships betweenthe hand and cricket velocity revealed that predictions of the expected future location can compensate for visuo-motor delays during fast reaching. These results suggest a critical role of visual prediction facilitatingonline movement adjustments for dynamic prey.

Event-Triggered Surrounding Formation Control of Multiagent Systems for Multiple Dynamic Targets

Event-Triggered Surrounding Formation Control of Multiagent Systems for Multiple Dynamic Targets

Designing Interference-Immune Doppler-Tolerant Waveforms for Radar Systems

Dynamic target detection using LFM waveform is challenging in the presence of interference for different radar applications. Degradation in SNR is irreparable and interference is difficult to mitigate in time and frequency domain. In this paper, a waveform design problem is addressed using the Majorization-Minimization (MM) framework by considering PSL/ISL cost functions, resulting in a code sequence with Doppler-tolerance characteristics of an LFM waveform and interference immune characteristics of a tailored polyphase sequence (unique phase code + minimal ISL/PSL). The optimal design sequences possess polynomial phase behavior of degree <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> amongst its sub-sequences and obtain optimal ISL and PSL solutions with guaranteed convergence. By tuning the optimization parameters such as degree <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> of the polynomial phase behavior, sub-sequence length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M$</tex-math></inline-formula> and the total number of sub-sequences <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L$</tex-math></inline-formula> , the optimized sequences can be as Doppler tolerant as LFM waveform in one end, and they can possess small cross-correlation values similar to random-phase sequences in polyphase sequence on the other end. The numerical results indicate that the proposed method is capable to computationally design chirplike sequences which prior to this work, were obtained by mimicking phase variations of LFM waveform. An application of the proposed method for the automotive scenario is also illustrated in the numerical results.

Doppler Positioning of Dynamic Targets with Unknown LEO Satellite Signals

A new concept of global navigation based on Doppler measurements from a large low Earth orbit (LEO) constellation is investigated that has potential to serve as a complement or backup to global navigation satellite systems (GNSSs) to provide navigation and positioning services in GNSS denial environments. In this work, we investigate the potential of LEO communication satellite opportunity signals in dynamic navigation, establish LEO satellite Doppler positioning equations, derive the main error sources affecting the Doppler positioning results of a dynamic target, and analyze the impact of each error source on positioning accuracy through a simulation. The results show that the orbit error and clock drift had a large impact on positioning accuracy. This navigation scheme would be more competitive if it could provide high-precision satellite orbits and accurate Doppler measurements. The obtained results show that the LEO satellite signals used as navigation opportunity signals are an attractive alternative in GNSS rejection environments for high dynamic targets.

Model following adaptive control for the complex dynamic network with the dynamic matrix‐relation links

SummaryThis article formulates and solves a problem of model following adaptive control (MFAC) of nodes in the complex dynamic network (CDN) with the dynamic matrix‐relation links. From the perspective of composite systems, the CDN with the dynamic matrix‐relation links is considered as an interconnected system composed of intercoupling node subsystem (NS) and link subsystem (LS). Based on this idea, employing the Riccati matrix differential equation as the dynamic equation of LS, and the vector differential equation with the second derivative term as the dynamic equation of NS. Then, to remove the restriction that the state of LS is unavailable, an adaptive control scheme is proposed for NS with the help of constructed dynamic auxiliary tracking target for LS, such that the problem of MFAC of nodes is solved. Besides, it is proved mathematically that the state of NS is asymptotically tracking the state of the given reference model when the state of LS is asymptotically tracking the constructed dynamic auxiliary tracking target. Finally, the numerical simulation is given to verify the effectiveness of the theoretical results in this article.

Geometric Constraint-Based and Improved YOLOv5 Semantic SLAM for Dynamic Scenes

When using deep learning networks for dynamic feature rejection in SLAM systems, problems such as a priori static object motion leading to disturbed build quality and accuracy and slow system runtime are prone to occur. In this paper, based on the ORB-SLAM2 system, we propose a method based on improved YOLOv5 networks combined with geometric constraint methods for SLAM map building in dynamic environments. First, this paper uses ShuffleNetV2 to lighten the YOLOv5 network, which increases the improved network’s operation speed without reducing the accuracy. At the same time, a pyramidal scene parsing network segmentation head is added to the head part of the YOLOv5 network to achieve semantic extraction in the environment, so that the improved YOLOv5 network has both target detection and semantic segmentation functions. In order to eliminate the objects with low dynamic features in the environment, this paper adopts the method of geometric constraints to extract and eliminate the dynamic features of the low dynamic objects. By combining the improved YOLOv5 network with the geometric constraint method, the robustness of the system is improved and the interference of dynamic targets in the construction of the SLAM system map is eliminated. The test results on the TUM dataset show that, when constructing a map in a dynamic environment, compared with the traditional ORB-SLAM2 algorithm, the accuracy of map construction in a dynamic environment is significantly improved. The absolute trajectory error is reduced by 97.7% compared with ORB-SLAM2, and the relative position error is reduced by 59.7% compared with ORB-SLAM2. Compared with DynaSLAM for dynamic scenes of the same type, the accuracy of map construction is slightly improved, but the maximum increase in keyframe processing time is 94.7%.

A novel bidding strategy based on dynamic targeting in real-time bidding market

A novel bidding strategy based on dynamic targeting in real-time bidding market

Association between Cerebrospinal Fluid Soluble TREM2, Alzheimer's Disease and Other Neurodegenerative Diseases.

Cerebrospinal fluid (CSF) soluble triggering receptor expressed on myeloid cells 2 (sTREM2) is a potential biomarker and therapy target for neurodegenerative diseases (NDDs). The purpose of this meta-analysis was to investigate the association between CSF sTREM2 level and NDDs, and to reveal the dynamic changes in CSF sTREM2 level in Alzheimer's disease (AD) continuum. We systematically searched PubMed, Embase, Web of Science, and Cochrane Library databases for observational studies, which compared the levels of CSF sTREM2 between NDDs and controls. Sources of heterogeneity were analyzed using sensitivity analysis, subgroup analysis and meta-regression. We assessed pooled data using a random-effects model. Twenty-two observational studies which included 5716 participates were identified. Compared with the controls, the whole AD continuum group showed a significant increase in CSF sTREM2 level (standardized mean difference [SMD]: 0.41, 95% confidence intervals [CI]: 0.24, 0.58, p < 0.001). The mild cognitive impairment (MCI) group displayed the largest effect size (SMD, 0.49 [95% CI: 0.10, 0.88], p = 0.014), followed by the AD cohort (SMD, 0.40 [95% CI: 0.18, 0.63], p < 0.001). The increase in sTREM2 in the preclinical stage of AD (pre-AD) group was the lowest (SMD, 0.29 [95% CI: 0.03, 0.55], p = 0.031). Other NDDs also showed an increase in the CSF sTREM2 levels compared with control groups (SMD, 0.77 [95% CI: 0.37, 1.16], p < 0.001). The pooled data confirmed that NDDs are associated with increased CSF sTREM2 level, thereby suggesting the CSF sTREM2 as a potential dynamic biomarker and therapy target for NDDs.

Delivering wildlife habitat on productive agricultural lands cost‐effectively: The case of migratory shorebirds on California rice lands

AbstractPromoting wildlife habitat on working agricultural land is a growing conservation priority, and agri‐environment schemes are using payments for environmental services (PES) to reach these privately owned lands. PES can be ecologically effective, but also expensive, putting pressure on these schemes to be cost‐effective, maximizing the conservation value of their limited resources. This study assesses the cost‐effectiveness of four PES schemes in California that paid rice growers to provide temporary flood habitat on their working lands in support of shorebirds. It examines whether the schemes (1) paid for flood habitat only when needed (targeting); (2) paid for habitat that was actually delivered and would not otherwise be provided (additionality); (3) sacrificed as little habitat as possible when the habitat competed with production (ag/wildlife balance); and (4) fostered a commitment among growers to maintain the habitat after payments ended (permanence). Results show that the schemes fell short in each of the four goals, and they expose several factors that undermined their cost‐effectiveness. First, variable weather patterns altered the date in which the shorebird‐habitat gap emerged year‐to‐year, creating a dynamic and unpredictable target for the schemes to address. Second, growers' views on the compatibility of the flood habitat with their rice production varied widely and changed rapidly as the agricultural calendar progressed, making it a challenge to mitigate potential ag/wildlife conflicts. Third, water for flood habitat is expensive, and growers proved unwilling to shoulder that expense once payments ended. These results highlight the need for schemes to adopt design elements that can add flexibility, such as cancelation clauses, so that they can adjust to dynamic targets and adapt to changing ag/wildlife relationships. Results also suggest that PES schemes operating on working lands may require long‐term external support. That finding amplifies the imperative that schemes use their conservation resources cost‐effectively.