Detecting the unpredictable: Advanced trajectory and camera-based anomaly detection methods for vehicles

Chakraborty, Neeloy

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https://hdl.handle.net/2142/120160 Copy

Description

Title

Detecting the unpredictable: Advanced trajectory and camera-based anomaly detection methods for vehicles

Author(s)

Chakraborty, Neeloy

Issue Date

2023-05-02

Keyword(s)

• Anomaly Detection
• Autonomous Vehicles
• Unsupervised Learning
• Human Behavior Modeling
• Deep Computer Vision

Abstract

In autonomous driving, detection of abnormal driving behaviors is essential to ensure the safety of vehicle controllers. Prior works in vehicle anomaly detection have shown that modeling interactions between agents improves detection accuracy, but certain abnormal behaviors where structured road information is paramount are poorly identified, such as wrong-way and off-road driving. We first propose a novel unsupervised framework for trajectory-based highway anomaly detection named Structural Attention-Based Recurrent VAE (SABeR-VAE), which explicitly uses the structure of the environment to aid anomaly identification. Specifically, we use a vehicle self-attention module to learn the relations among vehicles on a road, and a separate lane-vehicle attention module to model the importance of permissible lanes to aid in trajectory prediction. Conditioned on the attention modules' outputs, a recurrent encoder-decoder architecture with a stochastic Koopman operator-propagated latent space predicts the next states of vehicles. Our model is trained end-to-end to minimize prediction loss on normal vehicle behaviors, and is deployed to detect anomalies in (ab)normal scenarios. While SABeR-VAE outperforms state-of-the-art approaches, a pure trajectory sensor source is infeasible to access in real-world deployment. Thus, we then explore methods for camera-based anomaly detection conditioned on image sources. We apply convolutional autoencoder approaches and a Koopman model to the Detection of Traffic Anomaly (DoTA) dataset to evaluate methods on real anomalous scenarios captured on video.

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/120160

Copyright and License Information

Copyright 2023 Neeloy Chakraborty

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