Yifan Zhang

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Zhang

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Yifan

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0000-0001-8882-4327

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Publications 1 - 10 of 17

Detecting and identifying optical signal attacks on autonomous driving systems
Zhang, Jindi; Zhang, Yifan; Lu, Kejie; et al. (2021)
IEEE Internet of Things Journal
For autonomous driving, an essential task is to detect surrounding objects accurately. To this end, most existing systems use optical devices, including cameras and light detection and ranging (LiDAR) sensors, to collect environment data in real time. In recent years, many researchers have developed advanced machine learning models to detect surrounding objects. Nevertheless, the aforementioned optical devices are vulnerable to optical signal attacks, which could compromise the accuracy of object detection. To address this critical issue, we propose a framework to detect and identify sensors that are under attack. Specifically, we first develop a new technique to detect attacks on a system that consists of three sensors. Our main idea is to: 1) use data from three sensors to obtain two versions of depth maps (i.e., disparity) and 2) detect attacks by analyzing the distribution of disparity errors. In our study, we use real data sets and the state-of-the-art machine learning model to evaluate our attack detection scheme and the results confirm the effectiveness of our detection method. Based on the detection scheme, we further develop an identification model that is capable of identifying up to n-2 attacked sensors in a system with one LiDAR and n cameras. We prove the correctness of our identification scheme and conduct experiments to show the accuracy of our identification method. Finally, we investigate the overall sensitivity of our framework.
A Time-varying Shockwave Speed Model for Trajectory Reconstruction using Lagrangian and Eulerian Observations
Zhang, Yifan; Kouvelas, Anastasios; Makridis, Michail (2024)
Inference of detailed vehicle trajectories is crucial for applications such as traffic flow modeling, energy consumption estimation, and traffic flow optimization. Static sensors can provide only aggregated information, posing challenges in reconstructing individual vehicle trajectories. Shockwave theory is used to reproduce oscillations that occur between sensors. However, as the emerging of connected vehicles grows, probe data offers significant opportunities for more precise trajectory reconstruction. Existing methods rely on Eulerian observations (e.g., data from static sensors) and Lagrangian observations (e.g., data from connected vehicles) incorporating shockwave theory and car-following modeling. Despite these advancements, a prevalent issue lies in the static assignment of shockwave speed, which may not be able to reflect the traffic oscillations in a short time period caused by varying response times and vehicle dynamics. Moreover, driver dynamics while reconstructing the trajectories are ignored. In response, this paper proposes a novel framework that integrates Eulerian and Lagrangian observations for trajectory reconstruction on freeways. The approach introduces a calibration algorithm for time-varying shockwave speed. The shockwave speed calibrated by the CV is then utilized for trajectory reconstruction of other non-connected vehicles based on shockwave theory. Additionally, vehicle and driver dynamics are introduced to optimize the trajectory and estimate energy consumption by applying a vehicle movement model. The proposed method is evaluated using real-world datasets, demonstrating superior performance in terms of trajectory accuracy, reproducing traffic oscillations, and estimating energy consumption.
A Time-varying Shockwave Speed Model for Reconstructing Trajectories on Freeways using Lagrangian and Eulerian Observations
Zhang, Yifan; Kouvelas, Anastasios; Makridis, Michail (2024)
Expert Systems with Applications
Inference of detailed vehicle trajectories is crucial for applications such as traffic flow modeling, energy consumption estimation, and traffic flow optimization. Static sensors can provide only aggregated information, posing challenges in reconstructing individual vehicle trajectories. Shockwave theory is used to reproduce oscillations that occur between sensors. However, as the emerging of connected vehicles grows, probe data offers significant opportunities for more precise trajectory reconstruction. Existing methods rely on Eulerian observations (e.g., data from static sensors) and Lagrangian observations (e.g., data from connected vehicles) incorporating shockwave theory and car-following modeling. Despite these advancements, a prevalent issue lies in the static assignment of shockwave speed, which may not be able to reflect the traffic oscillations in a short time period caused by varying response times and vehicle dynamics. Moreover, driver dynamics while reconstructing the trajectories are ignored. In response, this paper proposes a novel framework that integrates Eulerian and Lagrangian observations for trajectory reconstruction on freeways. The approach introduces a calibration algorithm for time-varying shockwave speed. The shockwave speed calibrated by the CV is then utilized for trajectory reconstruction of other non-connected vehicles based on shockwave theory. Additionally, vehicle and driver dynamics are introduced to optimize the trajectory and estimate energy consumption by applying a vehicle movement model. The proposed method is evaluated using real-world datasets, demonstrating superior performance in terms of trajectory accuracy, reproducing traffic oscillations, and estimating energy consumption.
TOFG
Wen, Zihao; Zhang, Yifan; Chen, Xinhong; et al. (2023)
arXiv
In autonomous driving, an accurate understanding of environment, e.g., the vehicle-to-vehicle and vehicle-to-lane interactions, plays a critical role in many driving tasks such as trajectory prediction and motion planning. Environment information comes from high-definition (HD) map and historical trajectories of vehicles. Due to the heterogeneity of the map data and trajectory data, many data-driven models for trajectory prediction and motion planning extract vehicle-to-vehicle and vehicle-to-lane interactions in a separate and sequential manner. However, such a manner may capture biased interpretation of interactions, causing lower prediction and planning accuracy. Moreover, separate extraction leads to a complicated model structure and hence the overall efficiency and scalability are sacrificed. To address the above issues, we propose an environment representation, Temporal Occupancy Flow Graph (TOFG). Specifically, the occupancy flow-based representation unifies the map information and vehicle trajectories into a homogeneous data format and enables a consistent prediction. The temporal dependencies among vehicles can help capture the change of occupancy flow timely to further promote model performance. To demonstrate that TOFG is capable of simplifying the model architecture, we incorporate TOFG with a simple graph attention (GAT) based neural network and propose TOFG-GAT, which can be used for both trajectory prediction and motion planning. Experiment results show that TOFG-GAT achieves better or competitive performance than all the SOTA baselines with less training time.
The fragile nature of road transportation systems
Sun, Linghang; Zhang, Yifan; Axenie, Cristian; et al. (2024)
arXiv
Major cities worldwide experience problems with the performance of their road transportation systems. The continuous increase in traffic demand presents a substantial challenge to the optimal operation of urban road networks and the efficiency of traffic control strategies. Although robust and resilient transportation systems have been extensively researched over the past decades, their performance under an ever-growing traffic demand can still be questionable. The operation of transportation systems is widely believed to display fragile property, i.e., the loss in performance increases exponentially with the linearly increasing magnitude of disruptions, which undermines their continuous operation. The risk engineering community is now embracing the novel concept of (anti-)fragility, which enables systems to learn from historical disruptions and exhibit improved performance as disruption levels reach unprecedented magnitudes. In this study, we demonstrate the fragile nature of road transportation systems when faced with either demand or supply disruptions. First, we conducted a rigorous mathematical analysis to theoretically establish the fragile nature of the systems. Subsequently, by taking into account real-world stochasticity, we implemented a numerical simulation with realistic network data to bridge the gap between the theoretical proof and the real-world operations, to study the impact of uncertainty on the fragile property of the systems. This work aims to help researchers better comprehend the necessity to explicitly consider antifragile design toward the application of future traffic control strategies, coping with constantly growing traffic demand and subsequent traffic accidents.
CASAformer
Zhang, Yifan; Zhou, Qishen; Wang, Jianping; et al. (2025)
Communications in Transportation Research
Accurate and efficient traffic speed prediction is crucial for improving roaDongguand safety and efficiency. With the emerging deep learning and extensive traffic data, data-driven methods are widely adopted to achieve this task with increasingly complicated structures and progressively deeper layers of neural networks. Despite the design of the models, they aim to optimize the overall average performance without discriminating against different traffic states. However, the fact is that predicting the traffic speed under congestion is normally more important than under free flow since the downstream tasks, such as traffic control and optimization, are more interested in congestion rather than free flow. Most of the State-Of-The-Art (SOTA) models unfortunately do not differentiate between the traffic states during training and evaluation. To this end, we first comprehensively study the performance of the SOTA models under different speed regimes to illustrate the low accuracy of low-speed prediction. We further propose and design a novel Congestion-Aware Sparse Attention transformer (CASAformer) to enhance the prediction performance under low-speed traffic conditions. Specifically, the CASA layer emphasizes the congestion data and reduces the impact of free-flow data. Moreover, we adopt a new congestion adaptive loss function for training to make the model learn more from the congestion data. Extensive experiments on real-world datasets show that our CASAformer outperforms the SOTA models for predicting speed under 40 mph in all prediction horizons.
Evaluating and boosting reinforcement learning for intra-domain routing
Xu, Qian; Zhang, Yifan; Wu, Kui; et al. (2020)
Proceedings of the IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems (MASS 2019 )
The success of machine learning in domains such as computer vision and computer games has triggered a surge of interest in applying machine learning in computer networks. This paper tries to answer a broadly-debated question: can we improve the performance of intradomain routing, one of the most fundamental blocks in the Internet, with reinforcement learning (RL)? Due to the complex network traffic conditions and the large action space in routing, it is difficult to give a definite answer for existing RL-based routing solutions. To gain an in-depth understanding on the challenges of RL-based routing, we systematically classify different RL-based routing solutions and investigate the performance of several representative approaches, in terms of scalability, stability, robustness, and convergence. With the lessons learned in evaluating various RL-based routing solutions, we propose two methods, called supervised Q-network routing (SQR) and discrete link weight-based routing (DLWR), which boost the performance of RL-based routing and outperform the de facto shortest path intradomain routing.
A generative car-following model conditioned on driving styles
Zhang, Yifan; Chen, Xinhong; Wang, Jianping; et al. (2022)
Transportation Research Part C: Emerging Technologies
Car-following (CF) modeling, an essential component in simulating human CF behaviors, has attracted increasing research interest in the past decades. This paper pushes the state of the art by proposing a novel generative hybrid CF model, which achieves high accuracy in characterizing dynamic human CF behaviors and is able to generate realistic human CF behaviors for any given observed or even unobserved driving style. Specifically, the ability of accurately capturing human CF behaviors is ensured by designing and calibrating an Intelligent Driver Model (IDM) with time-varying parameters. The reason behind is that such time-varying parameters can express both the inter-driver heterogeneity, i.e., diverse driving styles of different drivers, and the intra-driver heterogeneity, i.e., changing driving styles of the same driver. The ability of generating realistic human CF behaviors of any given observed driving style is achieved by applying a neural process (NP) based model. The ability of inferring CF behaviors of unobserved driving styles is supported by exploring the relationship between the calibrated time-varying IDM parameters and an intermediate variable of NP. To demonstrate the effectiveness of our proposed models, we conduct extensive experiments and comparisons, including CF model parameter calibration, CF behavior prediction, and trajectory simulation for different driving styles.
The fragile nature of road transportation systems
Sun, Linghang; Zhang, Yifan; Axenie, Cristian; et al. (2024)
Major cities worldwide experience problems with the performance of their road transportation systems, and the continuous increase in traffic demand presents a substantial challenge to the optimal operation of urban road networks and the efficiency of traffic control strategies. Although robust and resilient transportation systems have been extensively researched over the past decades, their performance under an ever-growing traffic demand can still be questionable. The operation of transportation systems is widely believed to display fragile property, i.e., the loss in performance increases exponentially with the linearly increasing magnitude of disruptions, which undermines their continuous operation. Nowadays, the risk engineering community is embracing the novel concept of antifragility, which enables systems to learn from historical disruptions and exhibit improved performance as disruption levels reach unprecedented magnitudes. In this study, we demonstrate the fragile nature of road transportation systems when faced with demand or supply disruptions. First, we conducted a rigorous mathematical analysis to establish the fragile nature of the systems theoretically. Subsequently, by taking into account real-world stochasticity, we implemented a numerical simulation with realistic network data to bridge the gap between the theoretical proof and the real-world operations, to reflect the potential impact of uncertainty on the fragile property of the systems. This work aims to demonstrate the fragility of road transportation systems and help researchers better comprehend the necessity to explicitly consider antifragile design for future traffic control strategies, coping with the constantly growing traffic demand and subsequent traffic accidents.
A learning-based discretionary lane-change decision-making model with driving style awareness
Zhang, Yifan; Xu, Qian; Wang, Jianping; et al. (2023)
IEEE Transactions on Intelligent Transportation Systems
Discretionary lane change (DLC) is a basic but complex maneuver in driving, which aims at reaching a faster speed or better driving conditions, e.g., further line of sight or better ride quality. Although modeling DLC decision-making has been studied for years, the impact of human factors, which is crucial in accurately modelling human DLC decision-making strategies, is largely ignored in the existing literature. In this paper, we integrate the human factors that are represented by driving styles to design a new DLC decision-making model. Specifically, our proposed model takes not only the contextual traffic information but also the driving styles of surrounding vehicles into consideration and makes lane-change/keep decisions. Moreover, the model can imitate human drivers’ decision-making maneuvers by learning the driving style of the ego vehicle. Our evaluation results show that the proposed model captures the human decision-making strategies and imitates human drivers’ lane-change maneuvers, which can achieve 98.66% prediction accuracy. Moreover, we also analyze the lane-change impact of our model compared with human drivers in terms of improving the safety and speed of traffic.

Publications 1 - 10 of 17

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