Unsupervised Driving Event Discovery Based on Vehicle CAN-data

• URL: http://arxiv.org/abs/2301.04988v1
• Date: Thu, 12 Jan 2023 13:10:47 GMT
• Title: Unsupervised Driving Event Discovery Based on Vehicle CAN-data
• Authors: Thomas Kreutz, Ousama Esbel, Max M\"uhlh\"auser, Alejandro Sanchez Guinea
• Abstract summary: 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.
• Score: 62.997667081978825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The data collected from a vehicle's Controller Area Network (CAN) can quickly exceed human analysis or annotation capabilities when considering fleets of vehicles, which stresses the importance of unsupervised machine learning methods. This work presents a simultaneous clustering and segmentation approach for vehicle CAN-data that identifies common driving events in an unsupervised manner. The approach builds on self-supervised learning (SSL) for multivariate time series to distinguish different driving events in the learned latent space. 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. With our approach, we evaluate the applicability of recent time series-related contrastive and generative SSL techniques to learn representations that distinguish driving events. Compared to state-of-the-art (SOTA) generative SSL methods for driving event discovery, we find that contrastive learning approaches reach similar performance.

Related papers

• Federated Learning for Drowsiness Detection in Connected Vehicles [0.19116784879310028]
  Driver monitoring systems can assist in determining the driver's state. Driver drowsiness detection presents a potential solution. transmitting the data to a central machine for model training is impractical due to the large data size and privacy concerns. We propose a federated learning framework for drowsiness detection within a vehicular network, leveraging the YawDD dataset.
  arXiv  Detail & Related papers  (2024-05-06T09:39:13Z)
• 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)
• Learning Driver Models for Automated Vehicles via Knowledge Sharing and Personalization [2.07180164747172]
  This paper describes a framework for learning Automated Vehicles (AVs) driver models via knowledge sharing between vehicles and personalization. It finds several applications across transportation engineering including intelligent transportation systems, traffic management, and vehicle-to-vehicle communication.
  arXiv  Detail & Related papers  (2023-08-31T17:18:15Z)
• FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing [71.76084256567599]
  We present a system that enables an autonomous small-scale RC car to drive aggressively from visual observations using reinforcement learning (RL) Our system, FastRLAP (faster lap), trains autonomously in the real world, without human interventions, and without requiring any simulation or expert demonstrations. The resulting policies exhibit emergent aggressive driving skills, such as timing braking and acceleration around turns and avoiding areas which impede the robot's motion, approaching the performance of a human driver using a similar first-person interface over the course of training.
  arXiv  Detail & Related papers  (2023-04-19T17:33:47Z)
• ScaTE: A Scalable Framework for Self-Supervised Traversability Estimation in Unstructured Environments [7.226357394861987]
  In this work, we introduce a scalable framework for learning self-supervised traversability. We train a neural network that predicts the proprioceptive experience that a vehicle would undergo from 3D point clouds. With driving data of various vehicles gathered from simulation and the real world, we show that our framework is capable of learning the self-supervised traversability of various vehicles.
  arXiv  Detail & Related papers  (2022-09-14T09:52:26Z)
• Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification [168.67190934250868]
  Federated learning empowered connected autonomous vehicle (FLCAV) has been proposed. FLCAV preserves privacy while reducing communication and annotation costs. It is challenging to determine the network resources and road sensor poses for multi-stage training.
  arXiv  Detail & Related papers  (2022-06-03T23:55:45Z)
• Unsupervised Driving Behavior Analysis using Representation Learning and Exploiting Group-based Training [15.355045011160804]
  Driving behavior monitoring plays a crucial role in managing road safety and decreasing the risk of traffic accidents. Current work performs a robust driving pattern analysis by capturing variations in driving patterns. It forms consistent groups by learning compressed representation of time series.
  arXiv  Detail & Related papers  (2022-05-12T10:27:47Z)
• Learning Interactive Driving Policies via Data-driven Simulation [125.97811179463542]
  Data-driven simulators promise high data-efficiency for driving policy learning. Small underlying datasets often lack interesting and challenging edge cases for learning interactive driving. We propose a simulation method that uses in-painted ado vehicles for learning robust driving policies.
  arXiv  Detail & Related papers  (2021-11-23T20:14:02Z)
• Analysis of Driving Scenario Trajectories with Active Learning [6.316693022958221]
  We develop an active learning framework to annotate driving trajectory time-series data. We apply different active learning paradigms with different classification models to embedded data. We demonstrate how it can be used to identify the out-of-class trajectories.
  arXiv  Detail & Related papers  (2021-08-06T17:41:40Z)
• End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning [63.56464608571663]
  Navigating through intersections is one of the main challenging tasks for an autonomous vehicle. In this work, we focus on the implementation of a system able to navigate through intersections where only traffic signs are provided. We propose a multi-agent system using a continuous, model-free Deep Reinforcement Learning algorithm used to train a neural network for predicting both the acceleration and the steering angle at each time step.
  arXiv  Detail & Related papers  (2021-04-28T07:54:40Z)

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.