scholarly journals Simulation of AEBS Applicability by Changing Radar Detection Angle

2021 ◽  
Vol 11 (5) ◽  
pp. 2305
Author(s):  
Yongsoon Choi ◽  
Seryong Baek ◽  
Cheonho Kim ◽  
Junkyu Yoon ◽  
Seongkwan Mark Lee
Keyword(s):  
Autonomous Vehicles ◽  
Traffic Accidents ◽  
Smart Cities ◽  
Autonomous Driving ◽  
Radar Detection ◽  
Safe Driving ◽  
Euro Ncap ◽  
Test Standards ◽  
Emergency Braking ◽  
Crash Avoidance

As smart cities become a global topic, interest in smart mobility, the core of smart cities, is also growing. The technology that comes closest to general users is “autonomous driving”. In particular, the successful market entry and establishment of some private companies proved that “autonomous driving” is not technology of the future but imminent reality. However, safety in autonomous vehicles that rely on sensors instead of the driver’s five senses has been the focus of attention from the beginning and continues to be so. In this study, we attempted to counter this interest. Based on the actual data of thirty traffic accidents, assuming the AEBS (Autonomous Emergency Braking System) was installed to assist the driver in safe driving, it was reinterpreted through simulation to see what changes occurred in the accident. In the computer program, PC-Crash, the results were first analyzed through simulation using Euro NCAP (New Car Assessment Program)’s AEBS test standards. Subsequently, the other variables in the AEBS were controlled and the accident was reinterpreted by changing only the angle of the radar detection sensor. As a result, it was confirmed that a total of 27 accidents out of thirty accidents could have been prevented with the AEBS. In addition, it proved that the crash avoidance rate of vehicles gradually increased as the radar angle increased.

scholarly journals Skid resistance demands of asphalt pavement during the braking process of autonomous vehicles

2019 ◽  
Vol 275 ◽  
pp. 04002
Author(s):  
Zheng Binshuang ◽  
Chen Jiaying ◽  
Zhao Runmin ◽  
Huang Xiaoming
Keyword(s):  
Traffic Safety ◽  
Autonomous Vehicles ◽  
Traffic Accidents ◽  
Asphalt Pavement ◽  
Practical Significance ◽  
Skid Resistance ◽  
Safe Driving ◽  
Emergency Braking ◽  
Braking Distance ◽  
Technical Specifications

As the main operationality of AVs, the braking property is directly related to traffic safety. Major traffic accidents are often related to the braking distance, the side slip and hydroplaning during the emergency braking, which depends on the pavement skid resistance. Therefore, the estimation to relate AVs braking distance requirements with pavement peak friction coefficient to ensure a safe driving condition on expressway is of high practical significance. In this paper, the effect of AVs on braking performance parameters and dynamic friction on tire-pavement interaction are investigated. Based on the field test of the Coastal highway in Jiangsu province of China, this paper proposes an algorithm to determine time-dependent braking distance of AVs considering pavement frictional properties. According to the algorithm, an AVs braking system is provided to reach the maximum braking force for improving the AVs traffic safety. Furthermore, it revises the braking distance formula of Design Specification for Highway Alignment and the skid resistance threshold adopted by Technical Specifications for Maintenance of Highway Asphalt Pavement.

Towards Automated Bicycles: Achieving Self-Balance Using Steering Control

2018 ◽  
Author(s):  
Wenhao Deng ◽  
Skyler Moore ◽  
Jonathan Bush ◽  
Miles Mabey ◽  
Wenlong Zhang
Keyword(s):  
Autonomous Vehicles ◽  
Controller Design ◽  
Smart Cities ◽  
Autonomous Driving ◽  
Human Robot Interaction ◽  
Steering Control ◽  
Control Moment Gyroscope ◽  
Control Moment ◽  
Potential Applications ◽  
Electric Bicycle

In recent years, researchers from both academia and industry have worked on connected and automated vehicles and they have made great progress toward bringing them into reality. Compared to automated cars, bicycles are more affordable to daily commuters, as well as more environmentally friendly. When comparing the risk posed by autonomous vehicles to pedestrians and motorists, automated bicycles are much safer than autonomous cars, which also allows potential applications in smart cities, rehabilitation, and exercise. The biggest challenge in automating bicycles is the inherent problem of staying balanced. This paper presents a modified electric bicycle to allow real-time monitoring of the roll angles and motor-assisted steering. Stable and robust steering controllers for bicycle are designed and implemented to achieve self-balance at different forward speeds. Tests at different speeds have been conducted to verify the effectiveness of hardware development and controller design. The preliminary design using a control moment gyroscope (CMG) to achieve self-balancing at lower speeds are also presented in this work. This work can serve as a solid foundation for future study of human-robot interaction and autonomous driving.

scholarly journals Mode Switching Control Using Lane Keeping Assist and Waypoints Tracking for Autonomous Driving in a City Environment

2021 ◽  
pp. 036119812110566
Author(s):  
Nadjim Horri ◽  
Olivier Haas ◽  
Sheng Wang ◽  
Mathias Foo ◽  
Manuel Silverio Fernandez
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Mode Switching ◽  
Emergency Braking ◽  
Automatic Driving ◽  
Supervisory Controller ◽  
Multi Stage ◽  
Safety Constraints ◽  
Pure Pursuit ◽  
Lane Keeping

This paper proposes a mode switching supervisory controller for autonomous vehicles. The supervisory controller selects the most appropriate controller based on safety constraints and on the vehicle location with respect to junctions. Autonomous steering, throttle and deceleration control inputs are used to perform variable speed lane keeping assist, standard or emergency braking and to manage junctions, including roundabouts. Adaptive model predictive control with lane keeping assist is performed on the main roads and a linear pure pursuit inspired controller is applied using waypoints at road junctions where lane keeping assist sensors present a safety risk. A multi-stage rule based autonomous braking algorithm performs stop, restart and emergency braking maneuvers. The controllers are implemented in MATLAB® and Simulink™ and are demonstrated using the Automatic Driving Toolbox™ environment. Numerical simulations of autonomous driving scenarios demonstrate the efficiency of the lane keeping assist mode on roads with curvature and the ability to accurately track waypoints at cross intersections and roundabouts using a simpler pure pursuit inspired mode. The ego vehicle also autonomously stops in time at signaled intersections or to avoid collision with other road users.

scholarly journals Project ARES: Driverless Transportation System. Challenges and Approaches in an Unstructured Road

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1753
Author(s):  
Pablo Marin-Plaza ◽  
David Yagüe ◽  
Francisco Royo ◽  
Miguel Ángel de Miguel ◽  
Francisco Miguel Moreno ◽  
...  
Keyword(s):  
Urban Areas ◽  
Autonomous Vehicles ◽  
Smart Cities ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Vehicle Localization ◽  
Transport Efficiency ◽  
The Road ◽  
Operational Design ◽  
Low Coverage

The expansion of electric vehicles in urban areas has paved the way toward the era of autonomous vehicles, improving the performance in smart cities and upgrading related driving problems. This field of research opens immediate applications in the tourism areas, airports or business centres by greatly improving transport efficiency and reducing repetitive human tasks. This project shows the problems derived from autonomous driving such as vehicle localization, low coverage of 4G/5G and GPS, detection of the road and navigable zones including intersections, detection of static and dynamic obstacles, longitudinal and lateral control and cybersecurity aspects. The approaches proposed in this article are sufficient to solve the operational design of the problems related to autonomous vehicle application in the special locations such as rough environment, high slopes and unstructured terrain without traffic rules.

scholarly journals Policy-Gradient and Actor-Critic Based State Representation Learning for Safe Driving of Autonomous Vehicles

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 5991 ◽  
Author(s):  
Abhishek Gupta ◽  
Ahmed Shaharyar Khwaja ◽  
Alagan Anpalagan ◽  
Ling Guan ◽  
Bala Venkatesh
Keyword(s):  
Autonomous Vehicles ◽  
Representation Learning ◽  
Autonomous Driving ◽  
State Representation ◽  
Learning Loss ◽  
Safe Driving ◽  
Reward Function ◽  
Value Loss ◽  
Policy Gradient ◽  
Environment Perception

In this paper, we propose an environment perception framework for autonomous driving using state representation learning (SRL). Unlike existing Q-learning based methods for efficient environment perception and object detection, our proposed method takes the learning loss into account under deterministic as well as stochastic policy gradient. Through a combination of variational autoencoder (VAE), deep deterministic policy gradient (DDPG), and soft actor-critic (SAC), we focus on uninterrupted and reasonably safe autonomous driving without steering off the track for a considerable driving distance. Our proposed technique exhibits learning in autonomous vehicles under complex interactions with the environment, without being explicitly trained on driving datasets. To ensure the effectiveness of the scheme over a sustained period of time, we employ a reward-penalty based system where a negative reward is associated with an unfavourable action and a positive reward is awarded for favourable actions. The results obtained through simulations on DonKey simulator show the effectiveness of our proposed method by examining the variations in policy loss, value loss, reward function, and cumulative reward for ‘VAE+DDPG’ and ‘VAE+SAC’ over the learning process.

scholarly journals A visual approach towards forward collision warning for autonomous vehicles on Malaysian public roads

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 928
Author(s):  
Man Kiat Wong ◽  
Tee Connie ◽  
Michael Kah Ong Goh ◽  
Li Pei Wong ◽  
Pin Shen Teh ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Safe Driving ◽  
Collision Warning ◽  
Smart Transportation ◽  
The Road ◽  
Safety Mechanism ◽  
Visual Approach ◽  
On The Road ◽  
Forward Collision Warning

Background: Autonomous vehicles are important in smart transportation. Although exciting progress has been made, it remains challenging to design a safety mechanism for autonomous vehicles despite uncertainties and obstacles that occur dynamically on the road. Collision detection and avoidance are indispensable for a reliable decision-making module in autonomous driving. Methods: This study presents a robust approach for forward collision warning using vision data for autonomous vehicles on Malaysian public roads. The proposed architecture combines environment perception and lane localization to define a safe driving region for the ego vehicle. If potential risks are detected in the safe driving region, a warning will be triggered. The early warning is important to help avoid rear-end collision. Besides, an adaptive lane localization method that considers geometrical structure of the road is presented to deal with different road types. Results: Precision scores of mean average precision (mAP) 0.5, mAP 0.95 and recall of 0.14, 0.06979 and 0.6356 were found in this study. Conclusions: Experimental results have validated the effectiveness of the proposed approach under different lighting and environmental conditions.

scholarly journals Safe Driving Of Autonomous Vehicles Through Improved Deep Reinforcement Learning

2021 ◽  
Author(s):  
Abhishek Gupta
Keyword(s):  
Reinforcement Learning ◽  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Safe Driving ◽  
The Road ◽  
Reward Function ◽  
Continuous State ◽  
Value Loss ◽  
Policy Gradient

In this thesis, we propose an environment perception framework for autonomous driving using deep reinforcement learning (DRL) that exhibits learning in autonomous vehicles under complex interactions with the environment, without being explicitly trained on driving datasets. Unlike existing techniques, our proposed technique takes the learning loss into account under deterministic as well as stochastic policy gradient. We apply DRL to object detection and safe navigation while enhancing a self-driving vehicle’s ability to discern meaningful information from surrounding data. For efficient environmental perception and object detection, various Q-learning based methods have been proposed in the literature. Unlike other works, this thesis proposes a collaborative deterministic as well as stochastic policy gradient based on DRL. Our technique is a combination of variational autoencoder (VAE), deep deterministic policy gradient (DDPG), and soft actor-critic (SAC) that adequately trains a self-driving vehicle. In this work, we focus on uninterrupted and reasonably safe autonomous driving without colliding with an obstacle or steering off the track. We propose a collaborative framework that utilizes best features of VAE, DDPG, and SAC and models autonomous driving as partly stochastic and partly deterministic policy gradient problem in continuous action space, and continuous state space. To ensure that the vehicle traverses the road over a considerable period of time, we employ a reward-penalty based system where a higher negative penalty is associated with an unfavourable action and a comparatively lower positive reward is awarded for favourable actions. We also examine the variations in policy loss, value loss, reward function, and cumulative reward for ‘VAE+DDPG’ and ‘VAE+SAC’ over the learning process.

scholarly journals Estimation of the Closest In-Path Vehicle by Low-Channel LiDAR and Camera Sensor Fusion for Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3124
Author(s):  
Hyunjin Bae ◽  
Gu Lee ◽  
Jaeseung Yang ◽  
Gwanjun Shin ◽  
Gyeungho Choi ◽  
...  
Keyword(s):  
Cognitive Performance ◽  
Sensor Fusion ◽  
Autonomous Vehicles ◽  
Adaptive Cruise Control ◽  
Autonomous Driving ◽  
Fusion Method ◽  
Cruise Control ◽  
Test Protocol ◽  
Emergency Braking ◽  
Overall Stability

In autonomous driving, using a variety of sensors to recognize preceding vehicles at middle and long distances is helpful for improving driving performance and developing various functions. However, if only LiDAR or cameras are used in the recognition stage, it is difficult to obtain the necessary data due to the limitations of each sensor. In this paper, we proposed a method of converting the vision-tracked data into bird’s eye-view (BEV) coordinates using an equation that projects LiDAR points onto an image and a method of fusion between LiDAR and vision-tracked data. Thus, the proposed method was effective through the results of detecting the closest in-path vehicle (CIPV) in various situations. In addition, even when experimenting with the EuroNCAP autonomous emergency braking (AEB) test protocol using the result of fusion, AEB performance was improved through improved cognitive performance than when using only LiDAR. In the experimental results, the performance of the proposed method was proven through actual vehicle tests in various scenarios. Consequently, it was convincing that the proposed sensor fusion method significantly improved the adaptive cruise control (ACC) function in autonomous maneuvering. We expect that this improvement in perception performance will contribute to improving the overall stability of ACC.

scholarly journals Clothoid: An Integrated Hierarchical Framework for Autonomous Driving in a Dynamic Urban Environment

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5053 ◽  
Author(s):  
Saba Arshad ◽  
Muhammad Sualeh ◽  
Dohyeong Kim ◽  
Dinh Van Nam ◽  
Gon-Woo Kim
Keyword(s):  
Path Planning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Unified Framework ◽  
Human Errors ◽  
Safe Driving ◽  
Planning And Control ◽  
Starting Point ◽  
And Control

In recent years, research and development of autonomous driving technology have gained much interest. Many autonomous driving frameworks have been developed in the past. However, building a safely operating fully functional autonomous driving framework is still a challenge. Several accidents have been occurred with autonomous vehicles, including Tesla and Volvo XC90, resulting in serious personal injuries and death. One of the major reasons is the increase in urbanization and mobility demands. The autonomous vehicle is expected to increase road safety while reducing road accidents that occur due to human errors. The accurate sensing of the environment and safe driving under various scenarios must be ensured to achieve the highest level of autonomy. This research presents Clothoid, a unified framework for fully autonomous vehicles, that integrates the modules of HD mapping, localization, environmental perception, path planning, and control while considering the safety, comfort, and scalability in the real traffic environment. The proposed framework enables obstacle avoidance, pedestrian safety, object detection, road blockage avoidance, path planning for single-lane and multi-lane routes, and safe driving of vehicles throughout the journey. The performance of each module has been validated in K-City under multiple scenarios where Clothoid has been driven safely from the starting point to the goal point. The vehicle was one of the top five to successfully finish the autonomous vehicle challenge (AVC) in the Hyundai AVC.

scholarly journals Safe Driving Of Autonomous Vehicles Through Improved Deep Reinforcement Learning

2021 ◽  
Author(s):  
Abhishek Gupta
Keyword(s):  
Reinforcement Learning ◽  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Safe Driving ◽  
The Road ◽  
Reward Function ◽  
Continuous State ◽  
Value Loss ◽  
Policy Gradient

In this thesis, we propose an environment perception framework for autonomous driving using deep reinforcement learning (DRL) that exhibits learning in autonomous vehicles under complex interactions with the environment, without being explicitly trained on driving datasets. Unlike existing techniques, our proposed technique takes the learning loss into account under deterministic as well as stochastic policy gradient. We apply DRL to object detection and safe navigation while enhancing a self-driving vehicle’s ability to discern meaningful information from surrounding data. For efficient environmental perception and object detection, various Q-learning based methods have been proposed in the literature. Unlike other works, this thesis proposes a collaborative deterministic as well as stochastic policy gradient based on DRL. Our technique is a combination of variational autoencoder (VAE), deep deterministic policy gradient (DDPG), and soft actor-critic (SAC) that adequately trains a self-driving vehicle. In this work, we focus on uninterrupted and reasonably safe autonomous driving without colliding with an obstacle or steering off the track. We propose a collaborative framework that utilizes best features of VAE, DDPG, and SAC and models autonomous driving as partly stochastic and partly deterministic policy gradient problem in continuous action space, and continuous state space. To ensure that the vehicle traverses the road over a considerable period of time, we employ a reward-penalty based system where a higher negative penalty is associated with an unfavourable action and a comparatively lower positive reward is awarded for favourable actions. We also examine the variations in policy loss, value loss, reward function, and cumulative reward for ‘VAE+DDPG’ and ‘VAE+SAC’ over the learning process.

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