A Hardware-Friendly Optical Flow-Based Time-to-Collision Estimation Algorithm

Shi, Cong; Dong, Zhuoran; Pundlik, Shrinivas; Luo, Gang

doi:10.3390/s19040807

Cited by 7 publications

(10 citation statements)

References 32 publications

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“…The intersection of the 3D velocity vector characterizing the camera motion and the projection plane is represented by the FOE in the image plane. Time-to-impact estimation [51] and motion control [28], especially collision warning systems and obstacle avoidance, are prominent applications of FOE. In our implementation, we use the obtained optical flow with GF's algorithm and compute the estimated FOE.…”

Section: Focus Of Expansionmentioning

confidence: 99%

Optical Flow-Based Obstacle Detection for Mid-Air Collision Avoidance

Vera-Yanez,

Pereira,

Rodrigues

et al. 2024

Sensors

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The sky may seem big enough for two flying vehicles to collide, but the facts show that mid-air collisions still occur occasionally and are a significant concern. Pilots learn manual tactics to avoid collisions, such as see-and-avoid, but these rules have limitations. Automated solutions have reduced collisions, but these technologies are not mandatory in all countries or airspaces, and they are expensive. These problems have prompted researchers to continue the search for low-cost solutions. One attractive solution is to use computer vision to detect obstacles in the air due to its reduced cost and weight. A well-trained deep learning solution is appealing because object detection is fast in most cases, but it relies entirely on the training data set. The algorithm chosen for this study is optical flow. The optical flow vectors can help us to separate the motion caused by camera motion from the motion caused by incoming objects without relying on training data. This paper describes the development of an optical flow-based airborne obstacle detection algorithm to avoid mid-air collisions. The approach uses the visual information from a monocular camera and detects the obstacles using morphological filters, optical flow, focus of expansion, and a data clustering algorithm. The proposal was evaluated using realistic vision data obtained with a self-developed simulator. The simulator provides different environments, trajectories, and altitudes of flying objects. The results showed that the optical flow-based algorithm detected all incoming obstacles along their trajectories in the experiments. The results showed an F-score greater than 75% and a good balance between precision and recall.

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Section: Focus Of Expansionmentioning

confidence: 99%

Optical Flow-Based Obstacle Detection for Mid-Air Collision Avoidance

Vera-Yanez,

Pereira,

Rodrigues

et al. 2024

Sensors

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“…A collision time estimation algorithm proposed in [12] was shown to be accurate, hardware compatible, and potentially implementable on smart video sensor hardware. Using biological motion energy features and random forests, the algorithm estimates TTC from dense optical flow.…”

Section: Ai-based Techniquesmentioning

confidence: 99%

“…The use of the central limit theorem is discussed in relation to model reliability in [12]. Despite the potential for increased reliability with a larger dataset, ref.…”

Section: Open Areasmentioning

confidence: 99%

Collision Risk in Autonomous Vehicles: Classification, Challenges, and Open Research Areas

Goudarzi,

Hassanzadeh

2024

Vehicles

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When car following is controlled by human drivers (i.e., by their behavior), the traffic system does not meet stability conditions. In order to ensure the safety and reliability of self-driving vehicles, an additional hazard warning system should be incorporated into the adaptive control system in order to prevent any possible unavoidable collisions. The time to contact is a reasonable indicator of potential collisions. This research examines systems and solutions developed in this field to determine collision times and uses various alarms in self-driving cars that prevent collisions with obstacles. In the proposed analysis, we have tried to classify the various techniques and methods, including image processing, machine learning, deep learning, sensors, and so on, based on the solutions we have investigated. Challenges, future research directions, and open problems in this important field are also highlighted in the paper.

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“…Or in [25] an approach was presented for TTC and collision risk estimation in case of semi-rigid obstacles using videos of pedestrians captured in a controlled environment. However, such methods based on feature tracking and matching are not suitable for efficient hardware implementation [33].…”

Section: Related Workmentioning

confidence: 99%

“…The motion profiling used by the authors captures object motion, ignores most background objects, is more stable than optical flow, but is error-prone due to fake motion from shadows and reflections, camera shaking on uneven roads and camera tilting in vehicle breaking situations. In a closely related work, Shi et al [33] proposed a hardware-friendly TTC estimation method from the divergence of dense optical flow fields. They used random forests to compute optical flow from motion energy features.…”

Section: Related Workmentioning

confidence: 99%

Collision Avoidance Using Deep Learning-Based Monocular Vision

Rill

Faragó

2021

SN COMPUT. SCI.

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Autonomous driving technologies, including monocular vision-based approaches, are in the forefront of industrial and research communities, since they are expected to have a significant impact on economy and society. However, they have limitations in terms of crash avoidance because of the rarity of labeled data for collisions in everyday traffic, as well as due to the complexity of driving situations. In this work, we propose a simple method based solely on monocular vision to overcome the data scarcity problem and to promote forward collision avoidance systems. We exploit state-of-the-art deep learning-based optical flow and monocular depth estimation methods, as well as object detection to estimate the speed of the ego-vehicle and to identify the lead vehicle, respectively. The proposed method utilizes car stop situations as collision surrogates to obtain data for time to collision estimation. We evaluate this approach on our own driving videos, collected using a spherical camera and smart glasses. Our results indicate that similar accuracy can be achieved on both video sources: the external road view from the car’s, and the ego-centric view from the driver’s perspective. Additionally, we set forth the possibility of using spherical cameras as opposed to traditional cameras for vision-based automotive sensing.

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A Hardware-Friendly Optical Flow-Based Time-to-Collision Estimation Algorithm

Cited by 7 publications

References 32 publications

Optical Flow-Based Obstacle Detection for Mid-Air Collision Avoidance

Optical Flow-Based Obstacle Detection for Mid-Air Collision Avoidance

Collision Risk in Autonomous Vehicles: Classification, Challenges, and Open Research Areas

Collision Avoidance Using Deep Learning-Based Monocular Vision

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