arXiv subjects

Yong Shen

Publications and source records attributed to Yong Shen.

16 recordsLinked to original sources

arXiv2026

How defensive driving enhances driving safety: A driving simulator study on drivers' defensive driving behaviors

Defensive driving is widely recognized as an advanced driving skill. However, whether and how defensive driving affects driving safety remains insufficiently investigated. This study examines the behavioral characteristics of defensive driving, its impact on driving safety, and the underlying mechanisms. First, defensive driving is defined regarding operational timing and application scenario. Then, 82 participants are recruited for driving simulator experiments, with their behavioral and eye movement data being collected. Following the experiments, participants are categorized into groups based on the frequency of defensive driving behaviors exhibited. Finally, both inter-group and inter trial comparisons are performed on the experimental data. Experimental results demonstrate that in the inter-group comparison, the high defensive driving capability group exhibits higher acceleration and deceleration magnitudes, lower average speeds, and larger average absolute yaw angles compared to the low capability group, alongside shorter fixation durations and reduced fixation frequencies. Moreover, we observe that these participants tend to initiate defensive or evasive actions earlier, resulting in lower scenario risk. Regarding the inter-trial comparison, we observe similar trends exclusively in the low capability group, whereas most metrics show no significant differences between Trial 1 and Trial 2 in the high capability group. These results reveal that drivers possessing defensive driving capabilities tend to execute more intense driving maneuvers and identify risks and take action earlier, thereby enhancing driving safety. Findings support the promotion of defensive driving and provide a basis for relevant training programs. Meanwhile, they offer insights for the training of autonomous driving algorithms with defensive driving capabilities.

cs.RO↗

arXiv2026

Navigating heterogeneous protein landscapes through geometry-aware smoothing

The evolutionary fitness landscape of biological molecules is extremely sparse and heterogeneous, with functional sequences forming isolated dense ``islands'' within a vast combinatorial space of largely non-functional variants. Protein sequences, in particular, exemplify this structure, yet most generative artificial intelligence models implicitly assume a homogeneous data distribution. We show that this assumption fundamentally breaks down in heterogeneous biological sequence spaces: fixed global noise levels impose a destructive trade-off, either oversmoothing dense functional clusters or fragmenting sparse regions and producing non-functional hallucinations. To address this limitation, we introduce \emph{Density-Dependent Smoothing} (DDS), a geometry-aware generative framework that adapts stochastic smoothing to the local density of the underlying sequence landscape. By inversely coupling diffusion noise to estimated sequence density, DDS enables gentle refinement in high-density functional regions while promoting controlled exploration across sparse regions. Implemented as a plug-in mechanism for discrete molecular sampling, DDS consistently outperforms state-of-the-art diffusion and autoregressive models across antibody repertoires, therapeutic antibody design, antimicrobial peptide generation and coronavirus antibody design. Together, these results show that fixed global smoothing assumptions fundamentally limit generative modeling in sparse biological sequence spaces, and that geometry-aware smoothing removes this constraint, enabling reliable exploration and design previously unattainable with fixed-noise generative models.

cs.CE↗

arXiv2025

VLM as Strategist: Adaptive Generation of Safety-critical Testing Scenarios via Guided Diffusion

The safe deployment of autonomous driving systems (ADSs) relies on comprehensive testing and evaluation. However, safety-critical scenarios that can effectively expose system vulnerabilities are extremely sparse in the real world. Existing scenario generation methods face challenges in efficiently constructing long-tail scenarios that ensure fidelity, criticality, and interactivity, while particularly lacking real-time dynamic response capabilities to the vehicle under test (VUT). To address these challenges, this paper proposes a safety-critical testing scenario generation framework that integrates the high-level semantic understanding capabilities of Vision Language Models (VLMs) with the fine-grained generation capabilities of adaptive guided diffusion models. The framework establishes a three-layer hierarchical architecture comprising a strategic layer for VLM-directed scenario generation objective determination, a tactical layer for guidance function formulation, and an operational layer for guided diffusion execution. We first establish a high-quality fundamental diffusion model that learns the data distribution of real driving scenarios. Next, we design an adaptive guided diffusion method that enables real-time, precise control of background vehicles (BVs) in closed-loop simulation. The VLM is then incorporated to autonomously generate scenario generation objectives and guidance functions through deep scenario understanding and risk reasoning, ultimately guiding the diffusion model to achieve VLM-directed scenario generation. Experimental results demonstrate that the proposed method can efficiently generate realistic, diverse, and highly interactive safety-critical testing scenarios. Furthermore, case studies validate the adaptability and VLM-directed generation performance of the proposed method.

cs.RO↗

arXiv2025

An Evolving Scenario Generation Method based on Dual-modal Driver Model Trained by Multi-Agent Reinforcement Learning

In the autonomous driving testing methods based on evolving scenarios, the construction method of the driver model, which determines the driving maneuvers of background vehicles (BVs) in the scenario, plays a critical role in generating safety-critical scenarios. In particular, the cooperative adversarial driving characteristics between BVs can contribute to the efficient generation of safety-critical scenarios with high testing value. In this paper, a multi-agent reinforcement learning (MARL) method is used to train and generate a dual-modal driver model (Dual-DM) with non-adversarial and adversarial driving modalities. The model is then connected to a continuous simulated traffic environment to generate complex, diverse and strong interactive safety-critical scenarios through evolving scenario generation method. After that, the generated evolving scenarios are evaluated in terms of fidelity, test efficiency, complexity and diversity. Results show that without performance degradation in scenario fidelity (>85% similarity to real-world scenarios) and complexity (complexity metric: 0.45, +32.35% and +12.5% over two baselines), Dual-DM achieves a substantial enhancement in the efficiency of generating safety-critical scenarios (efficiency metric: 0.86, +195% over two baselines). Furthermore, statistical analysis and case studies demonstrate the diversity of safety-critical evolving scenarios generated by Dual-DM in terms of the adversarial interaction patterns. Therefore, Dual-DM can greatly improve the performance of the generation of safety-critical scenarios through evolving scenario generation method.

cs.LG↗

arXiv2025

RISEE: A Highly Interactive Naturalistic Driving Trajectories Dataset with Human Subjective Risk Perception and Eye-tracking Information

In the research and development (R&D) and verification and validation (V&V) phases of autonomous driving decision-making and planning systems, it is necessary to integrate human factors to achieve decision-making and evaluation that align with human cognition. However, most existing datasets primarily focus on vehicle motion states and trajectories, neglecting human-related information. In addition, current naturalistic driving datasets lack sufficient safety-critical scenarios while simulated datasets suffer from low authenticity. To address these issues, this paper constructs the Risk-Informed Subjective Evaluation and Eye-tracking (RISEE) dataset which specifically contains human subjective evaluations and eye-tracking data apart from regular naturalistic driving trajectories. By leveraging the complementary advantages of drone-based (high realism and extensive scenario coverage) and simulation-based (high safety and reproducibility) data collection methods, we first conduct drone-based traffic video recording at a highway ramp merging area. After that, the manually selected highly interactive scenarios are reconstructed in simulation software, and drivers' first-person view (FPV) videos are generated, which are then viewed and evaluated by recruited participants. During the video viewing process, participants' eye-tracking data is collected. After data processing and filtering, 3567 valid subjective risk ratings from 101 participants across 179 scenarios are retained, along with 2045 qualified eye-tracking data segments. The collected data and examples of the generated FPV videos are available in our website.

cs.HC↗

arXiv2025

Make Full Use of Testing Information: An Integrated Accelerated Testing and Evaluation Method for Autonomous Driving Systems

Testing and evaluation is an important step before the large-scale application of the autonomous driving systems (ADSs). Based on the three level of scenario abstraction theory, a testing can be performed within a logical scenario, followed by an evaluation stage which is inputted with the testing results of each concrete scenario generated from the logical parameter space. During the above process, abundant testing information is produced which is beneficial for comprehensive and accurate evaluations. To make full use of testing information, this paper proposes an Integrated accelerated Testing and Evaluation Method (ITEM). Based on a Monte Carlo Tree Search (MCTS) paradigm and a dual surrogates testing framework proposed in our previous work, this paper applies the intermediate information (i.e., the tree structure, including the affiliation of each historical sampled point with the subspaces and the parent-child relationship between subspaces) generated during the testing stage into the evaluation stage to achieve accurate hazardous domain identification. Moreover, to better serve this purpose, the UCB calculation method is improved to allow the search algorithm to focus more on the hazardous domain boundaries. Further, a stopping condition is constructed based on the convergence of the search algorithm. Ablation and comparative experiments are then conducted to verify the effectiveness of the improvements and the superiority of the proposed method. The experimental results show that ITEM could well identify the hazardous domains in both low- and high-dimensional cases, regardless of the shape of the hazardous domains, indicating its generality and potential for the safety evaluation of ADSs.

cs.AI↗

arXiv2024

LAMBDA: Covering the Multimodal Critical Scenarios for Automated Driving Systems by Search Space Quantization

Scenario-based virtual testing is one of the most significant methods to test and evaluate the safety of automated driving systems (ADSs). However, it is impractical to enumerate all concrete scenarios in a logical scenario space and test them exhaustively. Recently, Black-Box Optimization (BBO) was introduced to accelerate the scenario-based test of ADSs by utilizing the historical test information to generate new test cases. However, a single optimum found by the BBO algorithm is insufficient for the purpose of a comprehensive safety evaluation of ADSs in a logical scenario. In fact, all the subspaces representing danger in the logical scenario space, rather than only the most critical concrete scenario, play a more significant role for the safety evaluation. Covering as many of the critical concrete scenarios in a logical scenario space through a limited number of tests is defined as the Black-Box Coverage (BBC) problem in this paper. We formalized this problem in a sample-based search paradigm and constructed a coverage criterion with Confusion Matrix Analysis. Furthermore, we propose LAMBDA (Latent-Action Monte-Carlo Beam Search with Density Adaption) to solve BBC problems. LAMBDA can quickly focus on critical subspaces by recursively partitioning the logical scenario space into accepted and rejected parts. Compared with its predecessor LaMCTS, LAMBDA introduces sampling density to overcome the sampling bias from optimization and Beam Search to obtain more parallelizability. Experimental results show that LAMBDA achieves state-of-the-art performance among all baselines and can reach at most 33 and 6000 times faster than Random Search to get 95% coverage of the critical areas in 2- and 5-dimensional synthetic functions, respectively. Experiments also demonstrate that LAMBDA has a promising future in the safety evaluation of ADSs in virtual tests.

cs.AI↗

arXiv2024

Superposition- and interference-induced optical spectrum distortion in the figure-9 fiber laser

The output pulse spectra of the figure-8 and figure-9 lasers typically exhibit more pronounced distortion than those from mode-locked lasers based on other saturable absorbers, as well as the spectra of their own intracavity pulses. Here, we demonstrate two figure-9 lasers with repetition rates of 190.6 MHz and 92.4 MHz and introduce a self-designed beam splitter that exhibits minimal spectral filtering into the fiber loop to output two interference-free pulses. By applying superposition and interference calculations to the experimental spectra of these two pulses, we obtained calculated spectra and found that their characteristics agree with the distortion features observed in the experimental spectra from the other two ports where superposition and interference occur. Therefore, we conclude that the severe spectral distortion is caused by spectral superposition and interference, rather than the commonly believed nonlinear effects. Furthermore, analysis based on the interference theory of the figure-9 laser reveals that the $p$-components of the two intracavity light beams usually interfere with non-equal intensity at the beam splitter where interference occurs, while the $s$-components always interferes with almost equal intensity. This mechanism results in a significant yet stable spectral difference between the intracavity and output pulses. Moreover, a change in the pump power can amplify the difference between the two $s$-components, thereby leading to the emergence of a minor peak at the optical spectrum center. These findings provide new perspectives for simulating spectra that closely resemble experimental results and deepen our understanding of spectral evolution and pulse dynamics of the figure-9 lasers.

physics.optics↗

arXiv2024

Demonstrating two-particle interference with a one-dimensional delta potential well

In quantum mechanics, the exchange symmetry of wave functions for identical particles has observable effects, including the widely studied Hong-Ou-Mandel (HOM) effect. A theoretical description using second quantization is elegant but abstract. In contrast, this paper describes a simple model of two-particle interference using a one-dimensional delta potential well as a beam splitter. The conditions for the HOM effect are derived from the perspective of wave packet evolution. Furthermore, the interference processes of bosons, fermions and distinguishable particles are demonstrated and compared in detail. The method presented here is concrete, easy to visualize, and can help students to better understand the effects arising from the exchange symmetry of wave functions. The main results can be animated for classroom teaching or developed into an undergraduate seminar topic.

quant-ph↗

arXiv2024

In-situ aligned all-polarization-maintaining Er-doped fiber laser mode-locked by a nonlinear amplifying loop mirror

Despite the wide applications for high-repetition-rate mode-locked fiber lasers, challenges persist in shortening the cavity length and coupling the fiber collimators for most existing techniques. Here, we introduce a novel collimator alignment method and demonstrate an all-polarization-maintaining erbium-doped fiber laser that contains a nonlinear amplifying loop mirror with a repetition rate of 213 MHz. Compared to the conventional method, we achieve in-situ alignment of the collimators in a simplified two-step process. Besides, through a comparison of the spectra from the output ports of the laser, we assess their quality and establish the spectral evolution relationships among these ports. It is found that, in addition to the widely believed large nonlinear effects, spectral interference also plays a significant role in spectral distortion. Moreover, a transition between different stability states is observed from the power variation of the single pulse.

physics.optics↗

arXiv2024

Observation of periodic optical spectra and soliton molecules in a novel passively mode-locked fiber laser

Due to the necessity of making a series of random adjustments after mode-locking in most experiments for preparing soliton molecules, the repeatability of the preparations remains a challenge. Here, we introduce a novel all-polarization-maintaining erbium-doped fiber laser that utilizes a nonlinear amplifying loop mirror for mode-locking and features a linear shape. This laser can stably output soliton molecules without any additional adjustment once the mode-locking self-starts. Moreover, it can achieve the transition from soliton molecule state to soliton state, and then to multi-pulse state by reducing the pumping power. The unconventional method of generating multi-pulses, combined with a wide pumping power range of 200--640 mW for maintaining mode-locking, allowed us to observe periodic optical spectra with two complete cycles for the first time. Based on the experimental facts, we develop a multistability model to explain this phenomenon. With its ability to switch between three stable states, this flexible laser can serve as a versatile toolbox for studying soliton dynamics.

physics.optics↗

arXiv2022

Inverse saturable absorption mechanism and design optimization of mode-locked fiber lasers with a nonlinear amplifying loop mirror

From the point of view of the differential phase delay experienced by the two counterpropagating optical fields, the self-starting of the mode-locked fiber laser with a nonlinear amplifying loop mirror (NALM) is theoretically studied. Although it is generally believed that NALM shows a saturable absorption effect on both continuous wave (CW) light and pulses, we find a counter-intuitive fact that cross-phase modulation (XPM) leads to opposite signs of differential nonlinear phase shifts (NPSs) in these two cases, resulting in inverse saturable absorption (ISA) during pulse formation process. The ISA is not helpful for the self-starting of laser mode-locking and can be alleviated by introducing a non-reciprocal phase shifter into the fiber loop. In addition, we analyze the influences of gain-fiber position, splitting ratio, and optical attenuator in the fiber loop, on the differential NPS and self-starting process. These results are helpful for optimizing the design of NALM and lowering the self-starting threshold of the high-repetition-rate mode-locked fiber laser.

physics.optics↗

arXiv2015

Extraordinary focusing of sound above a soda can array without time reversal

Recently, Lemoult et al. [Phys. Rev. Lett. 107, 064301 (2011)] used time reversal to focus sound above an array of soda cans into a spot much smaller than the acoustic wavelength in air. In this study, we show that equally sharp focusing can be achieved without time reversal, by arranging transducers around a nearly circular array of soda cans. The size of the focal spot at the center of the array is made progressively smaller as the frequency approaches the Helmholtz resonance frequency of a can from below, and, near the resonance, becomes smaller than the size of a single can. We show that the locally resonant metamaterial formed by soda cans supports a guided wave at frequencies below the Helmholtz resonance frequency. The small focal spot results from a small wavelength of this guided wave near the resonance in combination with a near field effect making the acoustic field concentrate at the opening of a can. The focusing is achieved with propagating rather than evanescent waves. No sub-diffraction-limited focusing is observed if the diffraction limit is defined with respect to the wavelength of the guided mode in the metamaterial medium rather than the wavelength of the bulk wave in air.

physics.class-ph↗

arXiv2010

Experimental study on discretely modulated continuous-variable quantum key distribution

We present a discretely modulated continuous-variable quantum key distribution system in free space by using strong coherent states. The amplitude noise in the laser source is suppressed to the shot-noise limit by using a mode cleaner combined with a frequency shift technique. Also, it is proven that the phase noise in the source has no impact on the final secret key rate. In order to increase the encoding rate, we use broadband homodyne detectors and the no-switching protocol. In a realistic model, we establish a secret key rate of 46.8 kbits/s against collective attacks at an encoding rate of 10 MHz for a 90% channel loss when the modulation variance is optimal.

quant-ph↗

arXiv2010

Provable Secure Identity Based Generalized Signcryption Scheme

According to actual needs, generalized signcryption scheme can flexibly work as an encryption scheme, a signature scheme or a signcryption scheme. In this paper, firstly, we give a security model for identity based generalized signcryption which is more complete than existing model. Secondly, we propose an identity based generalized signcryption scheme. Thirdly, we give the security proof of the new scheme in this complete model. Comparing with existing identity based generalized signcryption, the new scheme has less implementation complexity. Moreover, the new scheme has comparable computation complexity with the existing normal signcryption schemes.

cs.CR↗

arXiv2009

Security bound of continuous-variable quantum key distribution with noisy coherent states and channel

Security of a continuous-variable quantum key distribution protocol based on noisy coherent states and channel is analyzed. Assuming the noise of coherent states is induced by Fred, a neutral party relative to others, we prove that the prepare and measurement scheme and entanglement-based scheme are equivalent. Then, we show that this protocol is secure against Gaussian collective attacks even if the channel is lossy and noisy, and further, a lower bound to the secure key rate is derived.

quant-ph↗