Vision-based Vehicle Speed Estimation for ITS: A Survey

• URL: http://arxiv.org/abs/2101.06159v1
• Date: Fri, 15 Jan 2021 15:07:54 GMT
• Title: Vision-based Vehicle Speed Estimation for ITS: A Survey
• Authors: David Fern\'andez Llorca, Antonio Hern\'andez Mart\'inez, Iv\'an Garc\'ia Daza
• Abstract summary: The number of speed cameras installed worldwide has been growing in recent years. Traffic monitoring and forecasting in road networks plays a fundamental role to enhance traffic, emissions and energy consumption in smart cities. The use of vision-based systems brings great challenges to be solved, but also great potential advantages.
• Score: 0.47248250311484113
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The need to accurately estimate the speed of road vehicles is becoming increasingly important for at least two main reasons. First, the number of speed cameras installed worldwide has been growing in recent years, as the introduction and enforcement of appropriate speed limits is considered one of the most effective means to increase the road safety. Second, traffic monitoring and forecasting in road networks plays a fundamental role to enhance traffic, emissions and energy consumption in smart cities, being the speed of the vehicles one of the most relevant parameters of the traffic state. Among the technologies available for the accurate detection of vehicle speed, the use of vision-based systems brings great challenges to be solved, but also great potential advantages, such as the drastic reduction of costs due to the absence of expensive range sensors, and the possibility of identifying vehicles accurately. This paper provides a review of vision-based vehicle speed estimation. We describe the terminology, the application domains, and propose a complete taxonomy of a large selection of works that categorizes all stages involved. An overview of performance evaluation metrics and available datasets is provided. Finally, we discuss current limitations and future directions.

Related papers

• Vehicle Speed Detection System Utilizing YOLOv8: Enhancing Road Safety and Traffic Management for Metropolitan Areas [0.0]
  Road accidents remain one of the leading causes of death in Bangladesh. The YOLOv8 model can recognize and track cars in videos with greater speed and accuracy when trained under close supervision.
  arXiv  Detail & Related papers  (2024-06-11T20:45:40Z)
• MSight: An Edge-Cloud Infrastructure-based Perception System for Connected Automated Vehicles [58.461077944514564]
  This paper presents MSight, a cutting-edge roadside perception system specifically designed for automated vehicles. MSight offers real-time vehicle detection, localization, tracking, and short-term trajectory prediction. Evaluations underscore the system's capability to uphold lane-level accuracy with minimal latency.
  arXiv  Detail & Related papers  (2023-10-08T21:32:30Z)
• Deep Reinforcement Learning for the Joint Control of Traffic Light Signaling and Vehicle Speed Advice [8.506271224735029]
  We propose a first attempt to jointly learn the control of both traffic light control and vehicle speed advice. In our experiments, the joint control approach reduces average vehicle trip delays, w.r.t. controlling only traffic lights, in eight out of eleven benchmark scenarios.
  arXiv  Detail & Related papers  (2023-09-18T15:45:22Z)
• Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving [68.95178518732965]
  A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants. Existing works either perform object detection followed by trajectory of the detected objects, or predict dense occupancy and flow grids for the whole scene. This motivates our unified approach to perception and future prediction that implicitly represents occupancy and flow over time with a single neural network.
  arXiv  Detail & Related papers  (2023-08-02T23:39:24Z)
• DenseLight: Efficient Control for Large-scale Traffic Signals with Dense Feedback [109.84667902348498]
  Traffic Signal Control (TSC) aims to reduce the average travel time of vehicles in a road network. Most prior TSC methods leverage deep reinforcement learning to search for a control policy. We propose DenseLight, a novel RL-based TSC method that employs an unbiased reward function to provide dense feedback on policy effectiveness.
  arXiv  Detail & Related papers  (2023-06-13T05:58:57Z)
• Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics [77.34726150561087]
  This work aims to carry out a study on the current scenario of camera and radar-based perception for ADAS and autonomous vehicles. Concepts and characteristics related to both sensors, as well as to their fusion, are presented. We give an overview of the Deep Learning-based detection and segmentation tasks, and the main datasets, metrics, challenges, and open questions in vehicle perception.
  arXiv  Detail & Related papers  (2023-03-08T00:48:32Z)
• Deep Learning-Based Vehicle Speed Prediction for Ecological Adaptive Cruise Control in Urban and Highway Scenarios [0.5161531917413706]
  In a typical car-following scenario, target vehicle speed fluctuations act as an external disturbance to the host vehicle and in turn affect its energy consumption. Deep recurrent neural network-based vehicle speed prediction using long-short term memory (LSTM) and gated recurrent units (GRU) is studied in this work. The proposed speed prediction models are evaluated for long-term predictions (up to 10 s) of target vehicle future velocities.
  arXiv  Detail & Related papers  (2022-11-30T22:50:43Z)
• A Hybrid Physics Machine Learning Approach for Macroscopic Traffic State Estimation [20.716261308570555]
  This paper introduces an innovative traffic state estimation framework. It uses limited information from traffic sensors as inputs to construct accurate and full-field estimated traffic state. Experimental results show that the proposed method has been proved to estimate full-field traffic information accurately.
  arXiv  Detail & Related papers  (2022-02-01T06:04:18Z)
• 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)
• Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving [104.32241082170044]
  We study a new task, safety-aware motion prediction with unseen vehicles for autonomous driving. Unlike the existing trajectory prediction task for seen vehicles, we aim at predicting an occupancy map. Our approach is the first one that can predict the existence of unseen vehicles in most cases.
  arXiv  Detail & Related papers  (2021-09-03T13:33:33Z)
• Two-Stream Networks for Lane-Change Prediction of Surrounding Vehicles [8.828423067460644]
  In highway scenarios, an alert human driver will typically anticipate early cut-in and cut-out maneuvers surrounding vehicles using only visual cues. To deal with lane-change recognition and prediction of surrounding vehicles, we pose the problem as an action recognition/prediction problem by stacking visual cues from video cameras. Two video action recognition approaches are analyzed: two-stream convolutional networks and multiplier networks.
  arXiv  Detail & Related papers  (2020-08-25T07:59:15Z)

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.