scholarly journals Simulation of Obstacle Detection of An Autonomous Car

2021 ◽  
pp. 430-435
Author(s):  
Rashmi Jain ◽  
Prachi Tamgade ◽  
R. Swaroopa ◽  
Pranoti Bhure ◽  
Srushti Shahu ◽  
...  
Keyword(s):  
Obstacle Detection ◽  
The Road ◽  
Virtual Self ◽  
Human Driver ◽  
Challenging Tasks ◽  
Video Input ◽  
Self Driving Cars

Perceiving the surroundings accurately and quickly is one of the most essential and challenging tasks for systems such as self-driving cars. view to the car making it more informed about the environment than a human driver. To build a fully virtual self-driving car, we have to build two things, Self-driving car software and virtual Self-driving car. Self-driving software can do two things one is based on video input of the road, the software can determine how to safely and effectively steer the car another is based on video input of the road, the software can determine how to safely and effectively use the car’s acceleration and braking mechanisms.

scholarly journals Design, manufacture and testing of a prototype obstacle-detection device at blind corners: an automated highway system technology

2020 ◽  
Author(s):  
Charles Atombo ◽  
Emmanuel Gbey ◽  
Apevienyeku Kwami Holali
Keyword(s):  
Traffic Accidents ◽  
Low Cost ◽  
Effective Area ◽  
Obstacle Detection ◽  
The Road ◽  
Vibration Sensors ◽  
Road Signs ◽  
Sensor Module ◽  
Vehicle Path ◽  
Automated Highway

Abstract Traffic accidents on highways are attributed mostly to the "invisibility" of oncoming traffic and road signs. "Speeding" also causes drivers to reduce the effective radius of the vehicle path in the curve, thus trespassing into the lane of the oncoming traffic. The main aim of this paper was to develop a multisensory obstacle-detection device that is affordable, easy to implement and easy to maintain to reduce the risk of road accidents at blind corners. An ultrasonic sensor module with a maximum measuring angle of 15° was used to ensure that a significant portion of the lane was detected at the blind corner. The sensor covered a minimum effective area of 0.5 m2 of the road for obstacle detection. Yellow light was employed to signify caution while negotiating the blind corner. Two photoresistors (PRs) were used as sensors because of the limited number of pins on the microcontroller (Arduino Uno). However, the device developed for this project achieved obstacle detection at blind corners at relatively low cost and can be accessed by all road users. In real-world applications, the use of piezoelectric accelerometers (vibration sensors) instead of PR sensors would be more desirable in order to detect not only cars but also two-wheelers.

Influence of Autonomous Vehicles on Car-Following Behavior of Human Drivers

2019 ◽  
Vol 2673 (12) ◽  
pp. 367-379 ◽  
Author(s):  
Yalda Rahmati ◽  
Mohammadreza Khajeh Hosseini ◽  
Alireza Talebpour ◽  
Benjamin Swain ◽  
Christopher Nelson
Keyword(s):  
Autonomous Vehicles ◽  
Simulation Models ◽  
Driving Behavior ◽  
Data Driven ◽  
Microscopic Simulation ◽  
Car Following ◽  
The Road ◽  
Human Driver ◽  
Significant Difference ◽  
On The Road

Despite numerous studies on general human–robot interactions, in the context of transportation, automated vehicle (AV)–human driver interaction is not a well-studied subject. These vehicles have fundamentally different decision-making logic compared with human drivers and the driving interactions between AVs and humans can potentially change traffic flow dynamics. Accordingly, through an experimental study, this paper investigates whether there is a difference between human–human and human–AV interactions on the road. This study focuses on car-following behavior and conducted several car-following experiments utilizing Texas A&M University’s automated Chevy Bolt. Utilizing NGSIM US-101 dataset, two scenarios for a platoon of three vehicles were considered. For both scenarios, the leader of the platoon follows a series of speed profiles extracted from the NGSIM dataset. The second vehicle in the platoon can be either another human-driven vehicle (scenario A) or an AV (scenario B). Data is collected from the third vehicle in the platoon to characterize the changes in driving behavior when following an AV. A data-driven and a model-based approach were used to identify possible changes in driving behavior from scenario A to scenario B. The findings suggested there is a statistically significant difference between human drivers’ behavior in these two scenarios and human drivers felt more comfortable following the AV. Simulation results also revealed the importance of capturing these changes in human behavior in microscopic simulation models of mixed driving environments.

scholarly journals Obstacle Detection and Safely Navigate the Autonomous Vehicle from Unexpected Obstacles on the Driving Lane

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4719
Author(s):  
Malik Haris ◽  
Jin Hou
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicle ◽  
Research Area ◽  
Visual Navigation ◽  
Obstacle Detection ◽  
The Road ◽  
Markov Random ◽  
Active Research ◽  
On The Road ◽  
Active Research Area

Nowadays, autonomous vehicle is an active research area, especially after the emergence of machine vision tasks with deep learning. In such a visual navigation system for autonomous vehicle, the controller captures images and predicts information so that the autonomous vehicle can safely navigate. In this paper, we first introduced small and medium-sized obstacles that were intentionally or unintentionally left on the road, which can pose hazards for both autonomous and human driving situations. Then, we discuss Markov random field (MRF) model by fusing three potentials (gradient potential, curvature prior potential, and depth variance potential) to segment the obstacles and non-obstacles into the hazardous environment. Since the segment of obstacles is done by MRF model, we can predict the information to safely navigate the autonomous vehicle form hazardous environment on the roadway by DNN model. We found that our proposed method can segment the obstacles accuracy from the blended background road and improve the navigation skills of the autonomous vehicle.

scholarly journals 3D visual perception for self-driving cars using a multi-camera system: Calibration, mapping, localization, and obstacle detection

2017 ◽  
Vol 68 ◽  
pp. 14-27 ◽  
Author(s):  
Christian Häne ◽  
Lionel Heng ◽  
Gim Hee Lee ◽  
Friedrich Fraundorfer ◽  
Paul Furgale ◽  
...  
Keyword(s):  
Visual Perception ◽  
Obstacle Detection ◽  
Camera System ◽  
System Calibration ◽  
Self Driving Cars

3D Reconstruction for Road Scene with Obstacle Detection Feedback

2018 ◽  
Vol 32 (12) ◽  
pp. 1855021 ◽  
Author(s):  
Huanbing Gao ◽  
Lei Liu ◽  
Ya Tian ◽  
Shouyin Lu
Keyword(s):  
3D Reconstruction ◽  
Laser Scanning ◽  
Autonomous Driving ◽  
Calibration Method ◽  
Obstacle Detection ◽  
Reconstruction Method ◽  
Navigation Systems ◽  
Driver Assistance ◽  
The Road ◽  
Angle Measuring

This paper presented 3D reconstruction method for road scene with the help of obstacle detection. 3D reconstruction for road scene can be used in autonomous driving, driver assistance system, car navigation systems. However, some errors often rose when 3D reconstructing due to the shade from the moving object in the road scene. The presented 3D reconstruction method with obstacle detection feedback can avoid this problem. Firstly, this paper offers a framework for the 3D reconstruction of road scene by laser scanning and vision. A calibration method based on the location of horizon is proposed, and a method of attitude angle measuring based on vanishing point is proposed to revise the 3D reconstruction result. Secondly, the reconstruction framework is extended by integrating with an object recognition that can automatically detect and discriminate obstacles in the input video streams by a RANSAC approach and threshold filter, and localizes them in the 3D model. 3D reconstruction and obstacle detection are tightly integrated and benefit from each other. The experiment result verified the feasibility and practicability of the proposed method.

A New Roadside Detection Method Based on Obstacle Detection

2012 ◽  
Vol 429 ◽  
pp. 324-328
Author(s):  
Chun He Yu ◽  
Dan Ping Zhang ◽  
Rui Guo
Keyword(s):  
Mobile Robot ◽  
High Stability ◽  
Detection Method ◽  
Detection Algorithm ◽  
Obstacle Detection ◽  
Laser Radar ◽  
The Road ◽  
Straight Lines

In order to provide road information for outdoor mobile robot in a complicated environment, a new roadside detection method is proposed based on obstacle detection by applying a four-layer laser radar LD_ML. Because roadside obstacles distribute alone a road, theirs fitting straight lines are parallel to the road. The roadsides detection algorithm includes four steps: first, judge if there are obstacles along roadside or not; second, extract obstacles which belong to roadsides; third, build fitting straight lines through the roadside obstacles; at last, in order to obtain steady and precise roadsides, a EKF method is performed to track the roadsides. The results of experiment have testified the road roadsides detection algorithm has high stability and reliability.

scholarly journals Improper Driver Action as a Cause of Road Accidents / Niewłaściwe Działania Kierowców Jako Przyczyna Powstawania Zdarzeń Drogowych

2012 ◽  
Vol 21 (1) ◽  
pp. 87-98
Author(s):  
Łukasz Muślewski
Keyword(s):  
Motor Vehicle ◽  
Road Traffic ◽  
Road Accident ◽  
Process Safety ◽  
Driving Experience ◽  
The Road ◽  
Human Driver ◽  
Proper Estimation ◽  
The Impact

Abstract Road traffic is inseparably connected with road accident. This is the human-driver whose role in the transportation process safety is of key importance. Driving a motor vehicle requires from the driver not only knowledge but also physical and psychical fitness. They need to have the ability of quick reaction, proper estimation of the road situation and doing maneuvers adequate to it. In this study, an assessment of the impact of improper behaviors of drivers on occurrence of road collisions and accidents, has been analyzed on the basis of literature analysis and the authors’ own research. In effect of the carried out tests there has been made a classification of the road events with a division into: cause, place, date, and time of their occurrence as well as drivers’ age and their driving experience. The whole study has been performed on the basis of a real transportation company, operating on the territory of an urban agglomeration with the population of 500 inhabitants.

scholarly journals Conceptual Design of an Anti-collision System for Light Rail Vehicles

2021 ◽  
Vol 12 (1) ◽  
pp. 9-26
Author(s):  
Mariusz WAŻNY ◽  
Krzysztof FALKOWSKI ◽  
Mirosław WRÓBLEWSKI ◽  
Konrad WOJTOWICZ ◽  
Adam MARUT
Keyword(s):  
Image Processing ◽  
Expert System ◽  
Communication System ◽  
Obstacle Detection ◽  
Light Rail ◽  
Collision System ◽  
Multiple Sources ◽  
The Road ◽  
Rail Vehicles ◽  
Visual Light

This paper presents the concepts for an anti-collision system intended for trams. The purpose of the anti-collision system is to develop and provide information to support the driver’s decision to initiate the braking of a tram. The anti-collision system is based on the processing of data from multiple sources (obstacle detection, image processing, and visual light communication system) and an expert system. The information about the road situation is visually presented on HUD (Head-up Display) of the driver.

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