scholarly journals Object and Traffic Light Recognition Model Development Using Multi-GPU Architecture for Autonomous Bus

2021 ◽  
Author(s):  
Jheanel Estrada ◽  
Gil Opina Jr ◽  
Anshuman Tripathi
Keyword(s):  
Object Detection ◽  
Public Transportation ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Model Development ◽  
Traffic Light ◽  
Night Time ◽  
Safety Perception ◽  
Acceptable Model ◽  
Gpu Architecture

The autonomous vehicle is both an exciting yet complex field to dig in these past few years. Many have ventured out to develop Level 4 Autonomous Vehicle but up to this point, many issues were still arising about its safety, perception and sensing capabilities, tracking, and localization. This paper aims to address the struggles of developing an acceptable model for object detection in real-time. Object detection is one of the challenging areas of autonomous vehicles due to the limitations of the camera, lidar, radar, and other sensors, especially during night-time. There were various datasets and models available, but the number of samples, the labels, the occlusions, and other factors may affect the performance of the dataset. To address the mentioned problem, this study has undergone a rigorous process of scene selection and imitation to deal with the imbalance dataset, applied the state-of-the-art YOLO architecture for the model development. After the development process, the model was deployed in a multi-GPU architecture that lessens the computational load on a single GPU structure and was tested on a 12-meter fully electric autonomous bus. This study will lead to the development of a usable and safe autonomous bus that will lead the future of public transportation.

Vision-Based Navigation of Autonomous Vehicles in Roadway Environments with Unexpected Hazards

2019 ◽  
Vol 2673 (12) ◽  
pp. 494-507 ◽  
Author(s):  
Mhafuzul Islam ◽  
Mashrur Chowdhury ◽  
Hongda Li ◽  
Hongxin Hu
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Semantic Segmentation ◽  
Steering Wheel ◽  
Potential Hazard ◽  
Driving System ◽  
Hazardous Object ◽  
Vision Based Navigation ◽  
Navigational System

Vision-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 in the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adverse inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicles by unexpected roadway hazards, such as debris or roadblocks. In this study, we first introduce a hazardous roadway environment that can compromise the DNN-based navigational system of an autonomous vehicle, and produce an incorrect steering wheel angle, which could cause crashes resulting in fatality or 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 hazard, 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 with the traditional DNN-based autonomous vehicle driving system.

scholarly journals A Game Theory-Based Approach for Modeling Autonomous Vehicle Behavior in Congested, Urban Lane-Changing Scenarios

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1523
Author(s):  
Nikita Smirnov ◽  
Yuzhou Liu ◽  
Aso Validi ◽  
Walter Morales-Alvarez ◽  
Cristina Olaverri-Monreal
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Urban Traffic ◽  
Road User ◽  
Lane Change ◽  
Traffic Light ◽  
User Interactions ◽  
Lane Changing

Autonomous vehicles are expected to display human-like behavior, at least to the extent that their decisions can be intuitively understood by other road users. If this is not the case, the coexistence of manual and autonomous vehicles in a mixed environment might affect road user interactions negatively and might jeopardize road safety. To this end, it is highly important to design algorithms that are capable of analyzing human decision-making processes and of reproducing them. In this context, lane-change maneuvers have been studied extensively. However, not all potential scenarios have been considered, since most works have focused on highway rather than urban scenarios. We contribute to the field of research by investigating a particular urban traffic scenario in which an autonomous vehicle needs to determine the level of cooperation of the vehicles in the adjacent lane in order to proceed with a lane change. To this end, we present a game theory-based decision-making model for lane changing in congested urban intersections. The model takes as input driving-related parameters related to vehicles in the intersection before they come to a complete stop. We validated the model by relying on the Co-AutoSim simulator. We compared the prediction model outcomes with actual participant decisions, i.e., whether they allowed the autonomous vehicle to drive in front of them. The results are promising, with the prediction accuracy being 100% in all of the cases in which the participants allowed the lane change and 83.3% in the other cases. The false predictions were due to delays in resuming driving after the traffic light turned green.

scholarly journals The City-Wide Impacts of the Interactions between Shared Autonomous Vehicle-Based Mobility Services and the Public Transportation System

2021 ◽  
Vol 13 (12) ◽  
pp. 6725
Author(s):  
Sehyun Tak ◽  
Soomin Woo ◽  
Sungjin Park ◽  
Sunghoon Kim
Keyword(s):  
Public Transportation ◽  
Autonomous Vehicles ◽  
Impact Analysis ◽  
Autonomous Vehicle ◽  
The Public ◽  
Private Vehicle ◽  
Simulation Based ◽  
Mode Share

When attempts are made to incorporate shared autonomous vehicles (SAVs) into urban mobility services, public transportation (PT) systems are affected by the changes in mode share. In light of that, a simulation-based method is presented herein for analyzing the manner in which mode choices of local travelers change between PT and SAVs. The data used in this study were the modal split ratios measured based on trip generation in the major cities of South Korea. Subsequently, using the simulated results, a city-wide impact analysis method is proposed that can reflect the differences between the two mode types with different travel behaviors. As the supply–demand ratio of SAVs increased in type 1 cities, which rely heavily on PT, use of SAVs gradually increased, whereas use of PT and private vehicles decreased. Private vehicle numbers significantly reduced only when SAVs and PT systems were complementary. In type 2 cities, which rely relatively less on PT, use of SAVs gradually increased, and use of private vehicles decreased; however, no significant impact on PT was observed. Private vehicle numbers were observed to reduce when SAVs were operated, and the reduction was a minimum of thrice that in type 1 cities when SAVs and PT systems interacted. Our results can therefore aid in the development of strategies for future SAV–PT operations.

scholarly journals Autonomous vehicles and smart cities: future directions of ownership vs shared mobility

2020 ◽  
Author(s):  
Nacer-Eddine Bezai ◽  
◽  
Benachir Medjdoub ◽  
Fodil Fadli ◽  
Moulay Larby Chalal ◽  
...  
Keyword(s):  
Public Transportation ◽  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Smart Cities ◽  
Autonomous Vehicle ◽  
Travel Speed ◽  
On Demand ◽  
Shared Mobility ◽  
The Future ◽  
Self Driving Cars

Over the last decade, there has been increasing discussions about self-driving cars and how most auto-makers are racing to launch these products. However, this discourse is not limited to transportation only, but how such vehicles will affect other industries and specific aspects of our daily lives as future users such as the concept of work while being driven and productivity, entertainment, travel speed, and deliveries. Although these technologies are beneficial, access to these potentials depends on the behaviour of their users. There is a lack of a conceptual model that elucidate the acceptance of people to Self-driving cars. Service on-demand and shared mobility are the most critical factors that will ensure the successful adoption of these cars. This paper presents an analysis of public opinions in Nottingham, UK, through a questionnaire about the future of Autonomous vehicles' ownership and the extent to which they accept the idea of vehicle sharing. Besides, this paper tests two hypotheses. Firstly, (a) people who usually use Public transportation like (taxi, bus, tram, train, carpooling) are likely to share an Autonomous Vehicle in the future. Secondly, (b) people who use Private cars are expected to own an Autonomous Vehicle in the future. To achieve this aim, a combination of statistical methods such as logistic regression has been utilised. Unexpectedly, the study findings suggested that AVs ownership will increase contrary to what is expected, that Autonomous vehicles will reduce ownership. Besides, participants have shown low interest in sharing AVs. Therefore, it is likely that ownership of AVs will increase for several reasons as expressed by the participants such as safety, privacy, personal space, suitability to children and availability. Actions must be taken to promote shared mobility to avoid AVs possession growth. The ownership diminution, in turn, will reduce traffic congestion, energy and transport efficiency, better air quality. That is why analysing the factors that influence the mindset and attitude of people will enable us to understand how to shift from private cars to transport-on-demand, which is a priority rather than promoting the technology.

scholarly journals Hybrid Multi-Sensor Integration for Static or Dynamic Obstacle Detection, Tracking and Classification for Autonomous Vehicle

2021 ◽  
Vol 23 (06) ◽  
pp. 1288-1293
Author(s):  
Dr. S. Rajkumar ◽  
◽  
Aklilu Teklemariam ◽  
Addisalem Mekonnen ◽  
◽  
...  
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Detection System ◽  
Autonomous Vehicle ◽  
Obstacle Detection ◽  
Multiple Sensors ◽  
Environment Perception ◽  
Hybrid Sensors ◽  
The Individual

Autonomous Vehicles (AV) reduces human intervention by perceiving the vehicle’s location with respect to the environment. In this regard, utilization of multiple sensors corresponding to various features of environment perception yields not only detection but also enables tracking and classification of the object leading to high security and reliability. Therefore, we propose to deploy hybrid multi-sensors such as Radar, LiDAR, and camera sensors. However, the data acquired with these hybrid sensors overlaps with the wide viewing angles of the individual sensors, and hence convolutional neural network and Kalman Filter (KF) based data fusion framework was implemented with a goal to facilitate a robust object detection system to avoid collisions inroads. The complete system tested over 1000 road scenarios for real-time environment perception showed that our hardware and software configurations outperformed numerous other conventional systems. Hence, this system could potentially find its application in object detection, tracking, and classification in a real-time environment.

scholarly journals Sensors in Autonomous Vehicles: A Survey

2021 ◽  
pp. 1-16
Author(s):  
Rodrigo Ayala ◽  
Tauheed Khan Mohd
Keyword(s):  
Public Transportation ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Ultrasonic Sensors ◽  
Advantages And Disadvantages ◽  
Smart Farming ◽  
Computer Scientists ◽  
Scientists And Engineers ◽  
Sensor Configuration

Abstract Research and technology in autonomous vehicles is beginning to become well recognized among computer scientists and engineers. Autonomous vehicles contain combination of GPS, LIDAR, cameras, RADAR and ultrasonic sensors (which are hardly ever included). These autonomous vehicles use no less than two sensing modalities, and usually have three or more. The goal of this research is to determine which sensor to use depending on the functionality of the autonomous vehicle and analyze the simi- larities and differences of sensor configurations (which may come from different industries too). This study summarizes sensors in four industries: personal vehicles, public transportation, smart farming, and logistics. In addition, the paper includes advantages and disadvantages of how each sensor configuration are helpful by taking into considerations the activity that has to be achieved in the autonomous vehicle. A table of results is incorporated to organize most of the sensors' availability in the market and their advantages and disadvantages. After comparing each sensor configuration, recommendations are going to be proposed for different scenarios in which some types of sensors will be more useful than others.

Privately Owned Autonomous Vehicle Optimization Model Development and Integration with Activity-Based Modeling and Dynamic Traffic Assignment Framework

2019 ◽  
Vol 2673 (10) ◽  
pp. 683-695
Author(s):  
Xiang Xu ◽  
Hani S. Mahmassani ◽  
Ying Chen
Keyword(s):  
Autonomous Vehicles ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Mode Choice ◽  
Autonomous Vehicle ◽  
Model Development ◽  
Area Network ◽  
Dynamic Traffic ◽  
Activity Schedules ◽  
The Impact

This paper presents a first-order approach integrated with activity-based modeling and dynamic traffic assignment framework to model the impact of autonomous vehicles on household travel and activity schedules. By considering shared rides among household members, mode choices, re-planning of departure times, and the rescheduling of activity sequences, two optimization models—basic personal owned autonomous vehicle (POAV) model and enhanced POAV model—are presented. The proposed approach is tested for the different models at the household level with different household sizes. The activity schedules of each household were generated in the Chicago sub-area network. The results show that each POAV can effectively replace multiple conventional vehicles, however, using POAV will lead to more vehicle miles traveled because of detour trips. The proposed enhanced POAV model considers mode choice decision with a household-based approach instead of a trip-based approach to capture the impacts of repositioning trips on mode choice. The results show that, if the generalized travel cost of POAV remains at the same level as conventional vehicles, more passengers will choose to use transit because the repositioning trips increase the total cost.

scholarly journals Opportunities and Challenges of Machine Vision for Navigation and Obstacle Detection in Autonomous Ground Vehicles

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1598-1601
Keyword(s):  
Machine Vision ◽  
Object Detection ◽  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Obstacle Detection ◽  
Conventional Vehicle ◽  
Object Detection And Tracking ◽  
Detection And Tracking ◽  
Autonomous Cars

Autonomous vehicles are the future of transport and also it is expected to become a fully-fledged reality within a decade. All the major giants in the automotive industry are hard pressing their transition from conventional vehicle to autonomous vehicles. The state of Karnataka, for instance, had approximately 205,200 registered taxis higher than Madhya Pradesh 174,900 registered cabs from 2014 to 2015. This presents a great deal of opportunities for autonomous cars and need for technologies. Autonomous cars reduces the accidents rate, stress free parking, saves time, reduces traffic congestion, improve fuel economy etc. It is so sophisticated to the level of easy prediction of physical objects, behavioural elements such as driving speed limits and driving rules between the physical world and its map. Autonomous vehicle have grown to an extent of updating its own information and also based on the cloud, benefitting the systems of all other cars on the network. Machine vision is the most crucial aspect which gives the autonomous vehicles the knowledge of its surrounding. This paper deals with the different approaches of machine vision that helps the vehicle in lane and obstacle detections. Few methods of obstacle detection like Single Object Detection and tracking (SODT) and Multiple Object Detection and tracking (MODT) are compared and contrasted in this paper. Despite the enormous advantages, there are still some challenges of autonomous which needs to be addressed. The challenges that the field will face, especially in relevance with India, along with the suggestion for improvement is also discussed.

Procuring cooperative intelligence in autonomous vehicles for object detection through data fusion approach

2020 ◽  
Vol 14 (11) ◽  
pp. 1410-1417 ◽  
Author(s):  
Alfred Daniel ◽  
Karthik Subburathinam ◽  
Bala Anand Muthu ◽  
Newlin Rajkumar ◽  
Seifedine Kadry ◽  
...  
Keyword(s):  
Data Fusion ◽  
Object Detection ◽  
Autonomous Vehicles ◽  
Fusion Approach

The human-like trajectory planning for autonomous vehicles based on optimal control in a test track environment

2021 ◽  
pp. 095440702110090
Author(s):  
Xing Xu ◽  
Minglei Li ◽  
Feng Wang ◽  
Ju Xie ◽  
Xiaohan Wu ◽  
...  
Keyword(s):  
Optimal Control ◽  
Autonomous Vehicles ◽  
Optimal Trajectory ◽  
Control Method ◽  
Autonomous Vehicle ◽  
Trajectory Data ◽  
Test Track ◽  
Optimal Control Method ◽  
The Future ◽  
On The Road

A human-like trajectory could give a safe and comfortable feeling for the occupants in an autonomous vehicle especially in corners. The research of this paper focuses on planning a human-like trajectory along a section road on a test track using optimal control method that could reflect natural driving behaviour considering the sense of natural and comfortable for the passengers, which could improve the acceptability of driverless vehicles in the future. A mass point vehicle dynamic model is modelled in the curvilinear coordinate system, then an optimal trajectory is generated by using an optimal control method. The optimal control problem is formulated and then solved by using the Matlab tool GPOPS-II. Trials are carried out on a test track, and the tested data are collected and processed, then the trajectory data in different corners are obtained. Different TLCs calculations are derived and applied to different track sections. After that, the human driver’s trajectories and the optimal line are compared to see the correlation using TLC methods. The results show that the optimal trajectory shows a similar trend with human’s trajectories to some extent when driving through a corner although it is not so perfectly aligned with the tested trajectories, which could conform with people’s driving intuition and improve the occupants’ comfort when driving in a corner. This could improve the acceptability of AVs in the automotive market in the future. The driver tends to move to the outside of the lane gradually after passing the apex when driving in corners on the road with hard-lines on both sides.

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