View Vertically: A Hierarchical Network for Trajectory Prediction via Fourier Spectrums

Wong, Conghao; Xia, Beihao; Hong, Ziming; Peng, Qinmu; Yuan, Wei; Cao, Qian; Yang, Yibo; You, Xinge

doi:10.1007/978-3-031-20047-2_39

Cited by 15 publications

(3 citation statements)

References 95 publications

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“…Transfer to other domains. The idea and the framework of BagAmmo are transferable to a certain degree, since many domains use semantic features and graph structured data (e.g., intrusion detection system [69] and trajectory prediction system [56,59]).…”

Section: Limitations and Discussionmentioning

confidence: 99%

Black-box Adversarial Example Attack towards FCG Based Android Malware Detection under Incomplete Feature Information

Li¹,

Zhang²,

Wu³

et al. 2023

Preprint

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The function call graph (FCG) based Android malware detection methods have recently attracted increasing attention due to their promising performance. However, these methods are susceptible to adversarial examples (AEs). In this paper, we design a novel black-box AE attack towards the FCG based malware detection system, called BagAmmo. To mislead its target system, BagAmmo purposefully perturbs the FCG feature of malware through inserting "never-executed" function calls into malware code. The main challenges are two-fold. First, the malware functionality should not be changed by adversarial perturbation. Second, the information of the target system (e.g., the graph feature granularity and the output probabilities) is absent.To preserve malware functionality, BagAmmo employs the try-catch trap to insert function calls to perturb the FCG of malware. Without the knowledge about feature granularity and output probabilities, BagAmmo adopts the architecture of generative adversarial network (GAN), and leverages a multi-population co-evolution algorithm (i.e., Apoem) to generate the desired perturbation. Every population in Apoem represents a possible feature granularity, and the real feature granularity can be achieved when Apoem converges.Through extensive experiments on over 44k Android apps and 32 target models, we evaluate the effectiveness, efficiency and resilience of BagAmmo. BagAmmo achieves an average attack success rate of over 99.9% on MaMaDroid, APIGraph and GCN, and still performs well in the scenario of concept drift and data imbalance. Moreover, BagAmmo outperforms the state-of-the-art attack SRL in attack success rate.

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Section: Limitations and Discussionmentioning

confidence: 99%

Black-box Adversarial Example Attack towards FCG Based Android Malware Detection under Incomplete Feature Information

Li¹,

Zhang²,

Wu³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…V 2 -Net [134] approaches the task of trajectory prediction not from a temporal sequence modeling point of view but from a spectral one. This method assumes that different frequency bands in the trajectory spectrum could represent an agent's motion preferences.…”

Section: Other Prediction Methodsmentioning

confidence: 99%

A Review of Trajectory Prediction Methods for the Vulnerable Road User

Schuetz,

Flohr

2023

Robotics

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Predicting the trajectory of other road users, especially vulnerable road users (VRUs), is an important aspect of safety and planning efficiency for autonomous vehicles. With recent advances in Deep-Learning-based approaches in this field, physics- and classical Machine-Learning-based methods cannot exhibit competitive results compared to the former. Hence, this paper provides an extensive review of recent Deep-Learning-based methods in trajectory prediction for VRUs and autonomous driving in general. We review the state and context representations and architectural insights of selected methods, divided into categories according to their primary prediction scheme. Additionally, we summarize reported results on popular datasets for all methods presented in this review. The results show that conditional variational autoencoders achieve the best overall results on both pedestrian and autonomous driving datasets. Finally, we outline possible future research directions for the field of trajectory prediction in autonomous driving.

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“…These approaches are primarily divided into kinetic and kinematic models (Lin, Ulsoy, and LeBlanc 2000). They use principles from physics and mechanics, taking into account the current state of the vehicle, such as speed and steering angle, to make predictions (Wong et al 2022). Despite their interpretability and computational efficiency, these methods often exhibit lower prediction accuracy compared to SOTA techniques .…”

Section: Related Workmentioning

confidence: 99%

BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving

Liao,

Li,

Shen

et al. 2024

AAAI

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The ability to accurately predict the trajectory of surrounding vehicles is a critical hurdle to overcome on the journey to fully autonomous vehicles. To address this challenge, we pioneer a novel behavior-aware trajectory prediction model (BAT) that incorporates insights and findings from traffic psychology, human behavior, and decision-making. Our model consists of behavior-aware, interaction-aware, priority-aware, and position-aware modules that perceive and understand the underlying interactions and account for uncertainty and variability in prediction, enabling higher-level learning and flexibility without rigid categorization of driving behavior. Importantly, this approach eliminates the need for manual labeling in the training process and addresses the challenges of non-continuous behavior labeling and the selection of appropriate time windows. We evaluate BAT's performance across the Next Generation Simulation (NGSIM), Highway Drone (HighD), Roundabout Drone (RounD), and Macao Connected Autonomous Driving (MoCAD) datasets, showcasing its superiority over prevailing state-of-the-art (SOTA) benchmarks in terms of prediction accuracy and efficiency. Remarkably, even when trained on reduced portions of the training data (25%), our model outperforms most of the baselines, demonstrating its robustness and efficiency in predicting vehicle trajectories, and the potential to reduce the amount of data required to train autonomous vehicles, especially in corner cases. In conclusion, the behavior-aware model represents a significant advancement in the development of autonomous vehicles capable of predicting trajectories with the same level of proficiency as human drivers. The project page is available on our GitHub.

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View Vertically: A Hierarchical Network for Trajectory Prediction via Fourier Spectrums

Cited by 15 publications

References 95 publications

Black-box Adversarial Example Attack towards FCG Based Android Malware Detection under Incomplete Feature Information

Black-box Adversarial Example Attack towards FCG Based Android Malware Detection under Incomplete Feature Information

A Review of Trajectory Prediction Methods for the Vulnerable Road User

BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving

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