scholarly journals Deep RetinaNet-Based Detection and Classification of Road Markings by Visible Light Camera Sensors

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 281 ◽  
Author(s):  
Toan Hoang ◽  
Phong Nguyen ◽  
Noi Truong ◽  
Young Lee ◽  
Kang Park
Keyword(s):  
Embedded System ◽  
Visible Light ◽  
Autonomous Vehicles ◽  
Complex Environments ◽  
Desktop Computer ◽  
The Road ◽  
On The Road ◽  
Open Datasets ◽  
Camera Sensors

Detection and classification of road markings are a prerequisite for operating autonomous vehicles. Although most studies have focused on the detection of road lane markings, the detection and classification of other road markings, such as arrows and bike markings, have not received much attention. Therefore, we propose a detection and classification method for various types of arrow markings and bike markings on the road in various complex environments using a one-stage deep convolutional neural network (CNN), called RetinaNet. We tested the proposed method in complex road scenarios with three open datasets captured by visible light camera sensors, namely the Malaga urban dataset, the Cambridge dataset, and the Daimler dataset on both a desktop computer and an NVIDIA Jetson TX2 embedded system. Experimental results obtained using the three open databases showed that the proposed RetinaNet-based method outperformed other methods for detection and classification of road markings in terms of both accuracy and processing time.

Road wetness quantification via tyre spray

2018 ◽  
Vol 233 (1) ◽  
pp. 28-37 ◽  
Author(s):  
Bernhard Schmiedel ◽  
Frank Gauterin ◽  
Hans-Joachim Unrau
Keyword(s):  
Vehicle Dynamics ◽  
Autonomous Vehicles ◽  
Water Film ◽  
Significant Loss ◽  
Vehicle Speed ◽  
The Road ◽  
Acceleration Sensors ◽  
On The Road ◽  
Wheel Arch

Road wetness can lead to a significant loss in tyre traction. Although a driver can easily distinguish between dry and wet roads, the thickness of a water film on the road (wetness) and its impact on the vehicle dynamics are more difficult for a driver to classify. Furthermore, autonomous vehicles also need a graded classification of road conditions. There are known sensors, which are able to classify road conditions, but these are either not able to quantify the road wetness or are not suitable for mass production. Therefore, this work analyses a method to measure the road wetness by analysing tyre spray with plain acceleration sensors at positions like wheel arch liner or side skirt. It discusses influences of vehicle speed, road wetness, tyres, road structure and sensor positioning. The results show that a quantification of road wetness is possible, but it relies on the sum of all boundary conditions.

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.

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.

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 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 Design and Fabrication of Steering Gear Mechanism with Movable Headlights Using Embedded Systems

2021 ◽  
pp. 180-187
Author(s):  
Kemparaju C.R. ◽  
Mohammed Nabeel Ahmed ◽  
B Meghanath ◽  
Mayur Laxman Kesarkar ◽  
Manoj DR
Keyword(s):  
Embedded System ◽  
Human Life ◽  
Harmful Effect ◽  
Steering System ◽  
Steering Wheel ◽  
The Road ◽  
Steering Mechanism ◽  
On The Road ◽  
Safety Features ◽  
The Individual

The main aim of any design must not solely be targeted on customer satisfaction however conjointly customer safety following this the amount of accidents are witness solely because of poor lighting facilities provided in automobiles on curved road static headlights are insufficient since they point tangential it along any point of curve instead of pointing in the vehicles direction so to avoid this problem steering controlled headlamp system has been projected which might hopefully flip out to be a boon to the individual driving through the sinusoidal roads throughout night times. Special safety features are built into cars for years some for the security of car’s occupants only, and some for the security of others. One among the alternatives available in design and fabrication of steering controlled headlight system. car safety is important to avoid automobile accidents or to minimise the harmful effect of accidents, especially as concerning human life and health. automobiles are controlled by incorporating steering controlled headlight mechanism. The Ackerman steering mechanism helps the motive force to guide the moving vehicles calls on the road by turning it right or left consistent with his needs thus a combination of the steering system and embedded system link kills the headlights within the direction as per the rotation of the steering wheel. this mechanism has been incorporated in BMW, Audi Q-7 and Benz etc., to make sure a safer drive, but our main aim is to implement the system in all vehicles at lower cost.

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.

scholarly journals CoDRIVE – Delivering High Accuracy, Ubiquitous Positioning Through Combined Radio Navigation and Inertial Sensing Technologies

2020 ◽  
Author(s):  
Simon Roberts
Keyword(s):  
Radio Frequency Identification ◽  
Autonomous Vehicles ◽  
Low Cost ◽  
Maximum Error ◽  
Precision Data ◽  
The Road ◽  
Frequency Identification ◽  
High Precision Data ◽  
On The Road ◽  
The University

The CoDRIVE solution builds on R&amp;amp;D in the development of connected and autonomous vehicles (CAVs). The mainstay of the system is a low-cost GNSS receiver integrated with a MEMS grade IMU powered with CoDRIVE algorithms and high precision data processing software. The solution integrates RFID (radio-frequency identification) localisation information derived from tags installed in the roads around the University of Nottingham. This aids the positioning solution by correcting the long-term drift of inertial navigation technology in the absence of GNSS. The solution is informed of obscuration of GNSS through city models of skyview and elevation masks derived from 360-degree photography. The results show that predictive intelligence of the denial of GNSS and RFID aiding realises significant benefits compared to the inertial only solution. According to the validation, inertial only solutions drift over time, with an overall RMS accuracy over a 300 metres section of GNSS outage of 10 to 20 metres. After deploying the RFID tags on the road, experiments show that the RFID aided algorithm is able to constrain the maximum error to within 3.76 metres, and with 93.9% of points constrained to 2 metres accuracy overall.

scholarly journals Future mobility as a bio-inspired collaborative system

2021 ◽  
Author(s):  
Naroa Coretti Sanchez ◽  
Juan Múgica Gonzalez ◽  
Luis Alonso Pastor ◽  
Kent Larson
Keyword(s):  
Autonomous Vehicles ◽  
Swarm Robotics ◽  
System Level ◽  
Urban Mobility ◽  
The Road ◽  
Collaborative System ◽  
Current Trends ◽  
Vehicle Platooning ◽  
On The Road ◽  
Open Question

The current trends towards vehicle-sharing, electrification, and autonomy are predicted to transform mobility. Combined appropriately, they have the potential of significantly improving urban mobility. However, what will come after most vehicles are shared, electric, and autonomous remains an open question, especially regarding the interactions between vehicles and how these interactions will impact system-level behaviour. Inspired by nature and supported by swarm robotics and vehicle platooning models, this paper proposes a future mobility in which shared, electric, and autonomous vehicles behave as a bio-inspired collaborative system. The collaboration between vehicles will lead to a system-level behaviour analogous to natural swarms. Natural swarms can divide tasks, cluster, build together, or transport cooperatively. In this future mobility, vehicles will cluster by connecting either physically or virtually, which will enable the possibility of sharing energy, data or computational power, provide services or transfer cargo, among others. Vehicles will collaborate either with vehicles that are part of the same fleet, or with any other vehicle on the road, by finding mutualistic relationships that benefit both parties. The field of swarm robotics has already translated some of the behaviours from natural swarms to artificial systems and, if we further translate these concepts into urban mobility, exciting ideas emerge. Within mobility-related research, the coordinated movement proposed in vehicle platooning models can be seen as a first step towards collaborative mobility. This paper contributes with the proposal of a framework for future mobility that integrates current research and mobility trends in a novel and unique way.

Sign in / Sign up

Export Citation Format

Share Document