Anticipating Driving Behavior through Deep Learning-Based Policy Prediction

• URL: http://arxiv.org/abs/2307.11058v3
• Date: Sun, 24 Sep 2023 17:56:19 GMT
• Title: Anticipating Driving Behavior through Deep Learning-Based Policy Prediction
• Authors: Alexander Liu
• Abstract summary: We developed a system that processes integrated visual features derived from video frames captured by a regular camera, along with depth details obtained from a point cloud scanner. This system is designed to anticipate driving actions, encompassing both vehicle speed and steering angle. Our evaluations indicate that the forecasts achieve a noteworthy level of accuracy in a minimum of half the test scenarios.
• Score: 66.344923925939
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this endeavor, we developed a comprehensive system that processes integrated visual features derived from video frames captured by a regular camera, along with depth details obtained from a point cloud scanner. This system is designed to anticipate driving actions, encompassing both vehicle speed and steering angle. To ensure its reliability, we conducted assessments where we juxtaposed the projected outcomes with the established norms adhered to by skilled real-world drivers. Our evaluation outcomes indicate that the forecasts achieve a noteworthy level of accuracy in a minimum of half the test scenarios (ranging around 50-80%, contingent on the specific model). Notably, the utilization of amalgamated features yielded superior performance in comparison to using video frames in isolation, as demonstrated by most of the cases.

Related papers

• Cross-Camera Distracted Driver Classification through Feature Disentanglement and Contrastive Learning [13.613407983544427]
  We introduce a robust model designed to withstand changes in camera position within the vehicle. Our Driver Behavior Monitoring Network (DBMNet) relies on a lightweight backbone and integrates a disentanglement module. Experiments conducted on the daytime and nighttime subsets of the 100-Driver dataset validate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-11-20T10:27:12Z)
• Enhancing End-to-End Autonomous Driving with Latent World Model [78.22157677787239]
  We propose a novel self-supervised method to enhance end-to-end driving without the need for costly labels. Our framework textbfLAW uses a LAtent World model to predict future latent features based on the predicted ego actions and the latent feature of the current frame. As a result, our approach achieves state-of-the-art performance in both open-loop and closed-loop benchmarks without costly annotations.
  arXiv  Detail & Related papers  (2024-06-12T17:59:21Z)
• Valeo4Cast: A Modular Approach to End-to-End Forecasting [93.86257326005726]
  Our solution ranks first in the Argoverse 2 End-to-end Forecasting Challenge, with 63.82 mAPf. We depart from the current trend of tackling this task via end-to-end training from perception to forecasting, and instead use a modular approach. We surpass forecasting results by +17.1 points over last year's winner and by +13.3 points over this year's runner-up.
  arXiv  Detail & Related papers  (2024-06-12T11:50:51Z)
• Robust Self-Supervised Extrinsic Self-Calibration [25.727912226753247]
  Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment. We introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning.
  arXiv  Detail & Related papers  (2023-08-04T06:20:20Z)
• Visual Saliency Detection in Advanced Driver Assistance Systems [7.455416595124159]
  We present an intelligent system that combines a drowsiness detection system for drivers with a scene comprehension pipeline based on saliency. We employ an innovative biosensor embedded on the car steering wheel to monitor the driver. A dedicated 1D temporal deep convolutional network has been devised to classify the collected PPG time-series.
  arXiv  Detail & Related papers  (2023-07-26T15:41:54Z)
• Non-Intrusive Driver Behavior Characterization From Road-Side Cameras [1.9659095632676098]
  We show a proof of concept for characterizing vehicular behavior using only the roadside cameras of ITS system. We show that the driver classification based on the external video analytics yields accuracies that are within 1-2% of the accuracies of direct vehicle characterization.
  arXiv  Detail & Related papers  (2023-02-25T17:22:49Z)
• Is my Driver Observation Model Overconfident? Input-guided Calibration Networks for Reliable and Interpretable Confidence Estimates [23.449073032842076]
  Driver observation models are rarely deployed under perfect conditions. We show that raw neural network-based approaches tend to significantly overestimate their prediction quality. We introduce Calibrated Action Recognition with Input Guidance (CARING)-a novel approach leveraging an additional neural network to learn scaling the confidences depending on the video representation.
  arXiv  Detail & Related papers  (2022-04-10T12:43:58Z)
• SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimation [101.55622133406446]
  We propose a SurroundDepth method to incorporate the information from multiple surrounding views to predict depth maps across cameras. Specifically, we employ a joint network to process all the surrounding views and propose a cross-view transformer to effectively fuse the information from multiple views. In experiments, our method achieves the state-of-the-art performance on the challenging multi-camera depth estimation datasets.
  arXiv  Detail & Related papers  (2022-04-07T17:58:47Z)
• Real Time Monocular Vehicle Velocity Estimation using Synthetic Data [78.85123603488664]
  We look at the problem of estimating the velocity of road vehicles from a camera mounted on a moving car. We propose a two-step approach where first an off-the-shelf tracker is used to extract vehicle bounding boxes and then a small neural network is used to regress the vehicle velocity.
  arXiv  Detail & Related papers  (2021-09-16T13:10:27Z)
• End-to-end Learning for Inter-Vehicle Distance and Relative Velocity Estimation in ADAS with a Monocular Camera [81.66569124029313]
  We propose a camera-based inter-vehicle distance and relative velocity estimation method based on end-to-end training of a deep neural network. The key novelty of our method is the integration of multiple visual clues provided by any two time-consecutive monocular frames. We also propose a vehicle-centric sampling mechanism to alleviate the effect of perspective distortion in the motion field.
  arXiv  Detail & Related papers  (2020-06-07T08:18:31Z)

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.