712 research outputs found
Vision-Based Navigation of Autonomous Vehicle in Roadway Environments with Unexpected Hazards
69A3551747117Vision-based navigation of autonomous vehicles primarily depends on the Deep Neural Network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems of the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adversarial inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicle by unexpected roadway hazards, such as debris and roadblocks. In this study, we first introduce a roadway hazardous environment (both intentional and unintentional roadway hazards) that can compromise the DNN-based navigational system of an autonomous vehicle, and produces an incorrect steering wheel angle, which can cause crashes resulting in fatality and injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazardous environment, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system including hazardous object detection and semantic segmentation improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared to the traditional DNN-based autonomous vehicle driving system
Controlling Steering Angle for Cooperative Self-driving Vehicles utilizing CNN and LSTM-based Deep Networks
A fundamental challenge in autonomous vehicles is adjusting the steering
angle at different road conditions. Recent state-of-the-art solutions
addressing this challenge include deep learning techniques as they provide
end-to-end solution to predict steering angles directly from the raw input
images with higher accuracy. Most of these works ignore the temporal
dependencies between the image frames. In this paper, we tackle the problem of
utilizing multiple sets of images shared between two autonomous vehicles to
improve the accuracy of controlling the steering angle by considering the
temporal dependencies between the image frames. This problem has not been
studied in the literature widely. We present and study a new deep architecture
to predict the steering angle automatically by using Long-Short-Term-Memory
(LSTM) in our deep architecture. Our deep architecture is an end-to-end network
that utilizes CNN, LSTM and fully connected (FC) layers and it uses both
present and futures images (shared by a vehicle ahead via Vehicle-to-Vehicle
(V2V) communication) as input to control the steering angle. Our model
demonstrates the lowest error when compared to the other existing approaches in
the literature.Comment: Accepted in IV 2019, 6 pages, 9 figure
The Potential for Autonomous Vehicle Technologies to Address Barriers to Driving for Individuals with Autism
Individuals with autism represent a sizeable share of the U.S. population (almost 2%), and nearly half of those with autism have average to high levels of intelligence. However, available research shows that adults with autism have a much more difficult time becoming employed and living independently compared to both typically developing adults and adults with disabilities. This study reviews the available literature on the magnitude of challenges to driving and accessing essential opportunities for adults with autism, and the potential of autonomous vehicles to address those challenges. This study is unique in that it identifies the specific driving challenges and needs faced by those with autism. The study makes the following recommendations: (1) Occupational therapists certified for driving rehabilitation should evaluate the driving abilities of those with autism and provide enhanced driver training, with and without autonomous vehicle technologies (i.e., warning systems, steering, acceleration/deceleration, and braking systems) to address any driving challenges; (2) If autonomous vehicle technology is shown in (1) to be necessary to allow for safe driving, then public funding should be made available to help with its purchase, just as funding is currently made available for those with physical disabilities to modify vehicles with adaptive equipment; (3) More tests of high automation should be conducted to affordably expand transit access; however, in the interim, public funding should be made available to subsidize ride-hailing services when transit is not a feasible travel option for those with autism; and (4) More research is needed to evaluate the effectiveness of autonomous vehicle technology interventions for driving (in 1) and expanding transit access (in 3)
DEVELOPMENT OF AN AUTONOMOUS NAVIGATION SYSTEM FOR THE SHUTTLE CAR IN UNDERGROUND ROOM & PILLAR COAL MINES
In recent years, autonomous solutions in the multi-disciplinary field of the mining engineering have been an extremely popular applied research topic. The growing demand for mineral supplies combined with the steady decline in the available surface reserves has driven the mining industry to mine deeper underground deposits. These deposits are difficult to access, and the environment may be hazardous to mine personnel (e.g., increased heat, difficult ventilation conditions, etc.). Moreover, current mining methods expose the miners to numerous occupational hazards such as working in the proximity of heavy mining equipment, possible roof falls, as well as noise and dust. As a result, the mining industry, in its efforts to modernize and advance its methods and techniques, is one of the many industries that has turned to autonomous systems. Vehicle automation in such complex working environments can play a critical role in improving worker safety and mine productivity.
One of the most time-consuming tasks of the mining cycle is the transportation of the extracted ore from the face to the main haulage facility or to surface processing facilities. Although conveyor belts have long been the autonomous transportation means of choice, there are still many cases where a discrete transportation system is needed to transport materials from the face to the main haulage system.
The current dissertation presents the development of a navigation system for an autonomous shuttle car (ASC) in underground room and pillar coal mines. By introducing autonomous shuttle cars, the operator can be relocated from the dusty, noisy, and potentially dangerous environment of the underground mine to the safer location of a control room. This dissertation focuses on the development and testing of an autonomous navigation system for an underground room and pillar coal mine.
A simplified relative localization system which determines the location of the vehicle relatively to salient features derived from on-board 2D LiDAR scans was developed for a semi-autonomous laboratory-scale shuttle car prototype. This simplified relative localization system is heavily dependent on and at the same time leverages the room and pillar geometry. Instead of keeping track of a global position of the vehicle relatively to a fixed coordinates frame, the proposed custom localization technique requires information regarding only the immediate surroundings. The followed approach enables the prototype to navigate around the pillars in real-time using a deterministic Finite-State Machine which models the behavior of the vehicle in the room and pillar mine with only a few states. Also, a user centered GUI has been developed that allows for a human user to control and monitor the autonomous vehicle by implementing the proposed navigation system.
Experimental tests have been conducted in a mock mine in order to evaluate the performance of the developed system. A number of different scenarios simulating common missions that a shuttle car needs to undertake in a room and pillar mine. The results show a minimum success ratio of 70%
Automated driving and autonomous functions on road vehicles
In recent years, road vehicle automation has become an important and popular topic for research
and development in both academic and industrial spheres. New developments received
extensive coverage in the popular press, and it may be said that the topic has captured the
public imagination. Indeed, the topic has generated interest across a wide range of academic,
industry and governmental communities, well beyond vehicle engineering; these include computer
science, transportation, urban planning, legal, social science and psychology. While this
follows a similar surge of interest – and subsequent hiatus – of Automated Highway Systems
in the 1990’s, the current level of interest is substantially greater, and current expectations
are high. It is common to frame the new technologies under the banner of “self-driving cars”
– robotic systems potentially taking over the entire role of the human driver, a capability that
does not fully exist at present. However, this single vision leads one to ignore the existing
range of automated systems that are both feasible and useful. Recent developments are underpinned
by substantial and long-term trends in “computerisation” of the automobile, with
developments in sensors, actuators and control technologies to spur the new developments in
both industry and academia. In this paper we review the evolution of the intelligent vehicle
and the supporting technologies with a focus on the progress and key challenges for vehicle
system dynamics. A number of relevant themes around driving automation are explored in
this article, with special focus on those most relevant to the underlying vehicle system dynamics.
One conclusion is that increased precision is needed in sensing and controlling vehicle
motions, a trend that can mimic that of the aerospace industry, and similarly benefit from
increased use of redundant by-wire actuators
Development of rear-end collision avoidance in automobiles
The goal of this work is to develop a Rear-End Collision Avoidance System for automobiles. In order to develop the Rear-end Collision Avoidance System, it is stated that the most important difference from the old practice is the fact that new design approach attempts to completely avoid collision instead of minimizing the damage by over-designing cars. Rear-end collisions are the third highest cause of multiple vehicle fatalities in the U.S. Their cause seems to be a result of poor driver awareness and communication. For example, car brake lights illuminate exactly the same whether the car is slowing, stopping or the driver is simply resting his foot on the pedal. In the development of Rear-End Collision Avoidance System (RECAS), a thorough review of hardware, software, driver/human factors, and current rear-end collision avoidance systems are included. Key sensor technologies are identified and reviewed in an attempt to ease the design effort. The characteristics and capabilities of alternative and emerging sensor technologies are also described and their performance compared. In designing a RECAS the first component is to monitor the distance and speed of the car ahead. If an unsafe condition is detected a warning is issued and the vehicle is decelerated (if necessary). The second component in the design effort utilizes the illumination of independent segments of brake lights corresponding to the stopping condition of the car. This communicates the stopping intensity to the following driver. The RECAS is designed the using the LabVIEW software. The simulation is designed to meet several criteria: System warnings should result in a minimum load on driver attention, and the system should also perform well in a variety of driving conditions.
In order to illustrate and test the proposed RECAS methods, a Java program has been developed. This simulation animates a multi-car, multi-lane highway environment where car speeds are assigned randomly, and the proposed RECAS approaches demonstrate rear-end collision avoidance successfully. The Java simulation is an applet, which is easily accessible through the World Wide Web and also can be tested for different angles of the sensor
Risk analysis of autonomous vehicle and its safety impact on mixed traffic stream
In 2016, more than 35,000 people died in traffic crashes, and human error was the reason for 94% of these deaths. Researchers and automobile companies are testing autonomous vehicles in mixed traffic streams to eliminate human error by removing the human driver behind the steering wheel. However, recent autonomous vehicle crashes while testing indicate the necessity for a more thorough risk analysis. The objectives of this study were (1) to perform a risk analysis of autonomous vehicles and (2) to evaluate the safety impact of these vehicles in a mixed traffic stream. The overall research was divided into two phases: (1) risk analysis and (2) simulation of autonomous vehicles. Risk analysis of autonomous vehicles was conducted using the fault tree method. Based on failure probabilities of system components, two fault tree models were developed and combined to predict overall system reliability. It was found that an autonomous vehicle system could fail 158 times per one-million miles of travel due to either malfunction in vehicular components or disruption from infrastructure components. The second phase of this research was the simulation of an autonomous vehicle, where change in crash frequency after autonomous vehicle deployment in a mixed traffic stream was assessed. It was found that average travel time could be reduced by about 50%, and 74% of conflicts, i.e., traffic crashes, could be avoided by replacing 90% of the human drivers with autonomous vehicles
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