Obstacle recognition for ADAS using stereovision and snake models

Liu, Shumin; Zhang, Ren‐Jie

doi:10.1109/itsc.2014.6957673

Cited by 9 publications

(6 citation statements)

References 20 publications

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“…Information regarding the dimensions can be employed to establish the type of vehicle, its class and eventually to derive a plausible mass. 33…”

Section: Methodsmentioning

confidence: 99%

“…Calculation time for the single simulation with the RODM software is about 1.5 s (i7 2600-based PC @ 3.4 GHz, 8GB RAM). For the single simulation, the velocity and the heading of the opponent vehicle are considered constant; in an actual implementation, if information is available from onboard sensors regarding the opponent’s size and mass, 33 the outcomes included in the database can be modified through appropriate corrective coefficients to account for different opponent category (e.g. sport-utility vehicles, vans, etc.…”

Section: Application Of the Adaptive Logicmentioning

confidence: 99%

“…Information regarding the dimensions can be employed to establish the type of vehicle, its class and eventually to derive a plausible mass. 33 The manoeuvre to perform must be selected as a function of predefined criteria: in such context, activation must primarily avert the collision, with a value of clearance (minimum distance reached between the vehicles during their motion) as high as possible; in case no manoeuvre can prevent the impact from occurring (ICS), the system must minimize IR for the occupants. The autonomous application of the manoeuvre chosen in terms of braking and steering is carried out by the electromechanical components of the vehicle, by systems as brake-by-wire or steer-by-wire.…”

Section: Model-in-the-loop Definitionmentioning

confidence: 99%

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Adaptive intervention logic for automated driving systems based on injury risk minimization

Vangi

Virga

Gulino

2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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Performance improvement of advanced driver assistance systems yields two major benefits: increasingly rapid progress towards autonomous driving and a simultaneous advance in vehicle safety. Integration of multiple advanced driver assistance systems leads to the so-called automated driving system, which can intervene jointly on braking and steering to avert impending crashes. Nevertheless, obstacles such as stationary vehicles and buildings can interpose between the opponent vehicles and the working field of advanced driver assistance systems’ sensors, potentially resulting in an inevitable collision state. Currently available devices cannot properly handle an inevitable collision state, because its occurrence is not subject to evaluations by the system. In the present work, criteria for intervention on braking and steering are introduced, based on the vehicle occupants’ injury risk. The system must monitor the surrounding and act on the degrees of freedom adapting to the evolution of the scenario, following an adaptive logic. The model-in-the-loop, software-in-the-loop and hardware-in-the-loop for such adaptive intervention are first introduced. To highlight the potential benefits offered by the adaptive advanced driver assistance systems, simulation software has been developed. The adaptive logic has been tested in correspondence of three inevitable collision state conditions between two motor vehicles: at each instant, the adaptive logic attitude of creating impact configurations associated with minimum injury risk is ultimately demonstrated.

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“…Information regarding the dimensions can be employed to establish the type of vehicle, its class and eventually to derive a plausible mass. 33…”

Section: Methodsmentioning

confidence: 99%

Section: Application Of the Adaptive Logicmentioning

confidence: 99%

Section: Model-in-the-loop Definitionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive intervention logic for automated driving systems based on injury risk minimization

Vangi

Virga

Gulino

2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…On the other hand, [28] fuses motion information (optical flow) with stereo vision. In addition, to detect specific types of obstacles, [29] combines the active contour model with stereo vision and a dimension-ratio based object classifier is used to distinguish pedestrians, vehicles and other objects.…”

Section: B Self-learning Processmentioning

confidence: 99%

Improved Situation Awareness for Autonomous Taxiing Through Self-Learning

Coombes

et al. 2016

IEEE Trans. Intell. Transport. Syst.

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As unmanned aerial vehicles (UAVs) become widely used in various civil applications, many civil aerodromes are being transformed into a hybrid environment for both manned and unmanned aircraft. In order to make these hybrid aerodromes operate safely and efficiently, the autonomous taxiing system of UAVs that adapts to the dynamic environment has now become increasingly important, particularly under poor visibility conditions. In this paper, we develop a probabilistic self-learning approach for the situation awareness of UAVs' autonomous taxiing. First, the probabilistic representation for a dynamic navigation map and camera images are developed at the pixel level to capture the taxiway markings and the other objects of interest (e.g., logistic vehicles and other aircraft). Then, we develop a self-learning approach so that the navigation map can be maintained online by continuously map-updating with the obtained camera observations via Bayesian learning. An indoor experiment was undertaken to evaluate the developed self-learning method for improved situation awareness. It shows that the developed approach is capable of improving the robustness of obstacle detection via updating the navigation map dynamically.

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“…Different sensor configurations such as a single camera [8], stereo vision [9], 2D [10] or 3D [11] laser scanners can be employed to assess motion. Mertz et al present a complete review of works that have developed detection and tracking of moving objects based on 2D laser scanners [12].…”

Section: Open Accessmentioning

confidence: 99%

Motion Detection from Mobile Robots with Fuzzy Threshold Selection in Consecutive 2D Laser Scans

Martínez

Morales

2015

Electronics

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Motion detection and tracking is a relevant problem for mobile robots during navigation to avoid collisions in dynamic environments or in applications where service robots interact with humans. This paper presents a simple method to distinguish mobile obstacles from the environment that is based on applying fuzzy threshold selection to consecutive two-dimensional (2D) laser scans previously matched with robot odometry.The proposed method has been tested with the Auriga-α mobile robot in indoors to estimate the motion of nearby pedestrians.

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Obstacle recognition for ADAS using stereovision and snake models

Cited by 9 publications

References 20 publications

Adaptive intervention logic for automated driving systems based on injury risk minimization

Adaptive intervention logic for automated driving systems based on injury risk minimization

Improved Situation Awareness for Autonomous Taxiing Through Self-Learning

Motion Detection from Mobile Robots with Fuzzy Threshold Selection in Consecutive 2D Laser Scans

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