Learning Representation for Anomaly Detection of Vehicle Trajectories
- URL: http://arxiv.org/abs/2303.05000v1
- Date: Thu, 9 Mar 2023 02:48:59 GMT
- Title: Learning Representation for Anomaly Detection of Vehicle Trajectories
- Authors: Ruochen Jiao, Juyang Bai, Xiangguo Liu, Takami Sato, Xiaowei Yuan, Qi
Alfred Chen and Qi Zhu
- Abstract summary: Predicting the future trajectories of surrounding vehicles based on their history trajectories is a critical task in autonomous driving.
Small crafted perturbations can significantly mislead the future trajectory prediction module of the ego vehicle.
We propose two novel methods for learning effective and efficient representations for online anomaly detection of vehicle trajectories.
- Score: 15.20257956793474
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Predicting the future trajectories of surrounding vehicles based on their
history trajectories is a critical task in autonomous driving. However, when
small crafted perturbations are introduced to those history trajectories, the
resulting anomalous (or adversarial) trajectories can significantly mislead the
future trajectory prediction module of the ego vehicle, which may result in
unsafe planning and even fatal accidents. Therefore, it is of great importance
to detect such anomalous trajectories of the surrounding vehicles for system
safety, but few works have addressed this issue. In this work, we propose two
novel methods for learning effective and efficient representations for online
anomaly detection of vehicle trajectories. Different from general time-series
anomaly detection, anomalous vehicle trajectory detection deals with much
richer contexts on the road and fewer observable patterns on the anomalous
trajectories themselves. To address these challenges, our methods exploit
contrastive learning techniques and trajectory semantics to capture the
patterns underlying the driving scenarios for effective anomaly detection under
supervised and unsupervised settings, respectively. We conduct extensive
experiments to demonstrate that our supervised method based on contrastive
learning and unsupervised method based on reconstruction with semantic latent
space can significantly improve the performance of anomalous trajectory
detection in their corresponding settings over various baseline methods. We
also demonstrate our methods' generalization ability to detect unseen patterns
of anomalies.
Related papers
- OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising [49.86409475232849]
Trajectory prediction is fundamental in computer vision and autonomous driving.
Existing approaches in this field often assume precise and complete observational data.
We present a novel method for out-of-sight trajectory prediction that leverages a vision-positioning technique.
arXiv Detail & Related papers (2024-04-02T18:30:29Z) - Self-Supervised Class-Agnostic Motion Prediction with Spatial and Temporal Consistency Regularizations [53.797896854533384]
Class-agnostic motion prediction methods directly predict the motion of the entire point cloud.
While most existing methods rely on fully-supervised learning, the manual labeling of point cloud data is laborious and time-consuming.
We introduce three simple spatial and temporal regularization losses, which facilitate the self-supervised training process effectively.
arXiv Detail & Related papers (2024-03-20T02:58:45Z) - Unsupervised Domain Adaptation for Self-Driving from Past Traversal
Features [69.47588461101925]
We propose a method to adapt 3D object detectors to new driving environments.
Our approach enhances LiDAR-based detection models using spatial quantized historical features.
Experiments on real-world datasets demonstrate significant improvements.
arXiv Detail & Related papers (2023-09-21T15:00:31Z) - A Memory-Augmented Multi-Task Collaborative Framework for Unsupervised
Traffic Accident Detection in Driving Videos [22.553356096143734]
We propose a novel memory-augmented multi-task collaborative framework (MAMTCF) for unsupervised traffic accident detection in driving videos.
Our method can more accurately detect both ego-involved and non-ego accidents by simultaneously modeling appearance changes and object motions in video frames.
arXiv Detail & Related papers (2023-07-27T01:45:13Z) - Unsupervised Driving Event Discovery Based on Vehicle CAN-data [62.997667081978825]
This work presents a simultaneous clustering and segmentation approach for vehicle CAN-data that identifies common driving events in an unsupervised manner.
We evaluate our approach with a dataset of real Tesla Model 3 vehicle CAN-data and a two-hour driving session that we annotated with different driving events.
arXiv Detail & Related papers (2023-01-12T13:10:47Z) - Semi-supervised Semantics-guided Adversarial Training for Trajectory
Prediction [15.707419899141698]
Adversarial attacks on trajectory prediction may mislead the prediction of future trajectories and induce unsafe planning.
We present a novel adversarial training method for trajectory prediction.
Our method can effectively mitigate the impact of adversarial attacks by up to 73% and outperform other popular defense methods.
arXiv Detail & Related papers (2022-05-27T20:50:36Z) - Trajectory Forecasting from Detection with Uncertainty-Aware Motion
Encoding [121.66374635092097]
Trajectories obtained from object detection and tracking are inevitably noisy.
We propose a trajectory predictor directly based on detection results without relying on explicitly formed trajectories.
arXiv Detail & Related papers (2022-02-03T09:09:56Z) - Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object
Detection [55.12894776039135]
State-of-the-art 3D object detectors, based on deep learning, have shown promising accuracy but are prone to over-fit to domain idiosyncrasies.
We propose a novel learning approach that drastically reduces this gap by fine-tuning the detector on pseudo-labels in the target domain.
We show, on five autonomous driving datasets, that fine-tuning the detector on these pseudo-labels substantially reduces the domain gap to new driving environments.
arXiv Detail & Related papers (2021-03-26T01:18:11Z) - Anomalous Motion Detection on Highway Using Deep Learning [14.617786106427834]
This paper presents a new anomaly detection dataset - the Highway Traffic Anomaly (HTA) dataset.
We evaluate state-of-the-art deep learning anomaly detection models and propose novel variations to these methods.
arXiv Detail & Related papers (2020-06-15T05:40:11Z) - VTGNet: A Vision-based Trajectory Generation Network for Autonomous
Vehicles in Urban Environments [26.558394047144006]
We develop an uncertainty-aware end-to-end trajectory generation method based on imitation learning.
Under various weather and lighting conditions, our network can reliably generate trajectories in different urban environments.
The proposed method achieves better cross-scene/platform driving results than the state-of-the-art (SOTA) end-to-end control method.
arXiv Detail & Related papers (2020-04-27T06:17:55Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.