scholarly journals A Grid-Based Framework for Collective Perception in Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 744
Author(s):  
Jorge Godoy ◽  
Víctor Jiménez ◽  
Antonio Artuñedo ◽  
Jorge Villagra
Keyword(s):  
Object Tracking ◽  
Autonomous Vehicles ◽  
Line Of Sight ◽  
The Road ◽  
Occupancy Grid ◽  
One Step ◽  
On The Road ◽  
High Level ◽  
Share Information ◽  
Grid Based

Today, perception solutions for Automated Vehicles rely on sensors on board the vehicle, which are limited by the line of sight and occlusions caused by any other elements on the road. As an alternative, Vehicle-to-Everything (V2X) communications allow vehicles to cooperate and enhance their perception capabilities. Besides announcing its own presence and intentions, services such as Collective Perception (CPS) aim to share information about perceived objects as a high-level description. This work proposes a perception framework for fusing information from on-board sensors and data received via CPS messages (CPM). To that end, the environment is modeled using an occupancy grid where occupied, and free and uncertain space is considered. For each sensor, including V2X, independent grids are calculated from sensor measurements and uncertainties and then fused in terms of both occupancy and confidence. Moreover, the implementation of a Particle Filter allows the evolution of cell occupancy from one step to the next, allowing for object tracking. The proposed framework was validated on a set of experiments using real vehicles and infrastructure sensors for sensing static and dynamic objects. Results showed a good performance even under important uncertainties and delays, hence validating the viability of the proposed framework for Collective Perception.

Predictive Control of Autonomous Ground Vehicles With Obstacle Avoidance on Slippery Roads

2010 ◽  
Author(s):  
Yiqi Gao ◽  
Theresa Lin ◽  
Francesco Borrelli ◽  
Eric Tseng ◽  
Davor Hrovat
Keyword(s):  
Obstacle Avoidance ◽  
Predictive Control ◽  
Autonomous Vehicles ◽  
Ground Vehicles ◽  
Vehicle Model ◽  
The Road ◽  
On Line ◽  
Nonlinear Vehicle Model ◽  
On The Road ◽  
High Level

Two frameworks based on Model Predictive Control (MPC) for obstacle avoidance with autonomous vehicles are presented. A given trajectory represents the driver intent. An MPC has to safely avoid obstacles on the road while trying to track the desired trajectory by controlling front steering angle and differential braking. We present two different approaches to this problem. The first approach solves a single nonlinear MPC problem. The second approach uses a hierarchical scheme. At the high-level, a trajectory is computed on-line, in a receding horizon fashion, based on a simplified point-mass vehicle model in order to avoid an obstacle. At the low-level an MPC controller computes the vehicle inputs in order to best follow the high level trajectory based on a nonlinear vehicle model. This article presents the design and comparison of both approaches, the method for implementing them, and successful experimental results on icy roads.

scholarly journals Brake systems: a mind of their own

2021 ◽  
pp. 27-34
Author(s):  
Michael Ellims
Keyword(s):  
Failure Modes ◽  
Electronic Control ◽  
Hydraulic Systems ◽  
Control Software ◽  
The Road ◽  
Current Production ◽  
On The Road ◽  
High Level ◽  
Electronic Sensors ◽  
Systems Failure

Brake systems fitted to current production vehicles are not the relativity straightforward hydraulic systems that many people expect. Rather they have evolved into complex systems which are on their own deliberately capable of affecting the behaviour of a vehicle. Crucially they depend on computers, software and electronic sensors to allow them to form a model of how the vehicle is expected to behave on the road and how it is actually behaving. Like any artefact they can, and do fail. This paper provides a high-level overview of the braking systems currently in place, how these systems act and present some examples of how they have failed in practice. Index words: vehicles; vehicle electronics; electronic control; software; brake systems; failure modes

The Aftermath of GRIEF

Grief ◽  
2020 ◽  
pp. 61-93
Author(s):  
David Shneer
Keyword(s):  
War Crimes ◽  
Soviet Power ◽  
Art Exhibitions ◽  
The Road ◽  
Nikita Khrushchev ◽  
War Crimes Trials ◽  
Fundamental Challenge ◽  
One Step ◽  
On The Road ◽  
India And China

This chapter is engaged with photography’s fundamental challenge: it is both documentary evidence, in which the camera is a tool of a technician, and a work of art, in which the click of the shutter is but one step on the road to creating an art photograph. It opens with Baltermants’s soaring career with Izvestiia until June 1942, when he was fired for mislabeling a photograph of a blown-up tank and was sent to a penal battalion that fought at Stalingrad. The experience nearly cost him his life. After the victory by the Allies, photographs were used as evidence in war crimes trials, including Baltermants’s. At the end of the war, he returned to photographic preeminence. After Stalin’s death, he became a leading documenter of global Soviet power under Nikita Khrushchev. He took pictures in Vietnam, India, and China, and his work appeared in domestic and international art exhibitions.

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.

V.—The Occurrence of Glacial Clay on the Cotteswold Plateau

1905 ◽  
Vol 2 (5) ◽  
pp. 216-219 ◽  
Author(s):  
C. Callaway
Keyword(s):  
The Road ◽  
Glacial Clay ◽  
On The Road ◽  
High Level

Mr. L. Richardson, F.G.S., having informed me that he had seen gravels at a high level on the road leading from Stow-on-the-Wold to Burford, I visited the locality, accompanied by Mr. J. W. Gray, F.G.S. About 3⅓ miles north of Burford, at the corner of the turning to Tangley, we came upon a deposit of clay with northern erratics in a quarry of oolite. As the position of such a formation was unexpected, and might be important, it seems desirable to record the discovery.

Analysis of Preference for Autonomous Driving Under Different Traffic Conditions Using a Driving Simulator

2015 ◽  
Vol 27 (6) ◽  
pp. 660-670 ◽  
Author(s):  
Udara Eshan Manawadu ◽  
◽  
Masaaki Ishikawa ◽  
Mitsuhiro Kamezaki ◽  
Shigeki Sugano ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Autonomous Driving ◽  
Driving Experience ◽  
The Road ◽  
Traffic Conditions ◽  
Passenger Vehicles ◽  
New Type ◽  
On The Road ◽  
Near Future

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270006/08.jpg"" width=""300"" /> Driving simulator</div>Intelligent passenger vehicles with autonomous capabilities will be commonplace on our roads in the near future. These vehicles will reshape the existing relationship between the driver and vehicle. Therefore, to create a new type of rewarding relationship, it is important to analyze when drivers prefer autonomous vehicles to manually-driven (conventional) vehicles. This paper documents a driving simulator-based study conducted to identify the preferences and individual driving experiences of novice and experienced drivers of autonomous and conventional vehicles under different traffic and road conditions. We first developed a simplified driving simulator that could connect to different driver-vehicle interfaces (DVI). We then created virtual environments consisting of scenarios and events that drivers encounter in real-world driving, and we implemented fully autonomous driving. We then conducted experiments to clarify how the autonomous driving experience differed for the two groups. The results showed that experienced drivers opt for conventional driving overall, mainly due to the flexibility and driving pleasure it offers, while novices tend to prefer autonomous driving due to its inherent ease and safety. A further analysis indicated that drivers preferred to use both autonomous and conventional driving methods interchangeably, depending on the road and traffic conditions.

scholarly journals The Application of Image Processing to Solve Occlusion Issue in Object Tracking

2018 ◽  
Vol 152 ◽  
pp. 03001
Author(s):  
Yun Zhe Cheong ◽  
Wei Jen Chew
Keyword(s):  
Object Tracking ◽  
Moving Objects ◽  
Object Segmentation ◽  
Target Object ◽  
Occlusion Handling ◽  
Multiple Objects ◽  
The Road ◽  
Occluded Objects ◽  
On The Road ◽  
Tracking Object

Object tracking is a computer vision field that involves identifying and tracking either a single or multiple objects in an environment. This is extremely useful to help observe the movements of the target object like people in the street or cars on the road. However, a common issue with tracking an object in an environment with many moving objects is occlusion. Occlusion can cause the system to lose track of the object being tracked or after overlapping, the wrong object will be tracked instead. In this paper, a system that is able to correctly track occluded objects is proposed. This system includes algorithms such as foreground object segmentation, colour tracking, object specification and occlusion handling. An input video is input to the system and every single frame of the video is analysed. The foreground objects are segmented with object segmentation algorithm and tracked with the colour tracking algorithm. An ID is assigned to each tracked object. Results obtained shows that the proposed system is able to continuously track an object and maintain the correct identity even after is has been occluded by another object.

Telomerase activation: one step on the road to cancer?

1999 ◽  
Vol 15 (3) ◽  
pp. 109-112 ◽  
Author(s):  
Carol W Greider
Keyword(s):  
The Road ◽  
One Step ◽  
On The Road

scholarly journals Multi-Classifier Feature Fusion-Based Road Detection for Connected Autonomous Vehicles

2021 ◽  
Vol 11 (17) ◽  
pp. 7984
Author(s):  
Prabu Subramani ◽  
Khalid Nazim Abdul Sattar ◽  
Rocío Pérez de Prado ◽  
Balasubramanian Girirajan ◽  
Marcin Wozniak
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicles ◽  
Feature Fusion ◽  
Domain Adaptation ◽  
Road Detection ◽  
Convolutional Network ◽  
The Road ◽  
Learning Classifiers ◽  
On The Road ◽  
Institute Of Technology

Connected autonomous vehicles (CAVs) currently promise cooperation between vehicles, providing abundant and real-time information through wireless communication technologies. In this paper, a two-level fusion of classifiers (TLFC) approach is proposed by using deep learning classifiers to perform accurate road detection (RD). The proposed TLFC-RD approach improves the classification by considering four key strategies such as cross fold operation at input and pre-processing using superpixel generation, adequate features, multi-classifier feature fusion and a deep learning classifier. Specifically, the road is classified as drivable and non-drivable areas by designing the TLFC using the deep learning classifiers, and the detected information using the TLFC-RD is exchanged between the autonomous vehicles for the ease of driving on the road. The TLFC-RD is analyzed in terms of its accuracy, sensitivity or recall, specificity, precision, F1-measure and max F measure. The TLFC- RD method is also evaluated compared to three existing methods: U-Net with the Domain Adaptation Model (DAM), Two-Scale Fully Convolutional Network (TFCN) and a cooperative machine learning approach (i.e., TAAUWN). Experimental results show that the accuracy of the TLFC-RD method for the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset is 99.12% higher than its competitors.

scholarly journals Contested Spaces, Shared Concerns

2020 ◽  
Vol 9 (2) ◽  
pp. 155-191
Author(s):  
Sarah Stutts ◽  
Kenneth Saintonge ◽  
Nicholas Jordan ◽  
Christina Wasson
Keyword(s):  
Autonomous Vehicles ◽  
Road User ◽  
North Texas ◽  
University Of North Texas ◽  
Contested Spaces ◽  
The Road ◽  
Driving Behaviors ◽  
Road Users ◽  
On The Road ◽  
The University

Roadways are sociocultural spaces constructed for human travel which embody intersections of technology, transportation, and culture. In order to navigate these spaces successfully, autonomous vehicles must be able to respond to the needs and practices of those who use the road. We conducted research on how cyclists, solid waste truck drivers, and crossing guards experience the driving behaviors of other road users, to inform the development of autonomous vehicles. We found that the roadways were contested spaces, with each road user group enacting their own social constructions of the road. Furthermore, the three groups we worked with all felt marginalized by comparison with car drivers, who were ideologically and often physically dominant on the road. This article is based on research for the Nissan Research Center - Silicon Valley, which took place as part of a Design Anthropology course at the University of North Texas.

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