REEEC-AGENT: human driver cognition and emotions-inspired rear-end collision avoidance method for autonomous vehicles

SIMULATION ◽  
2021 ◽  
pp. 003754972110047
Author(s):  
Muhammad A Butt ◽  
Faisal Riaz ◽  
Yasir Mehmood ◽  
Somyyia Akram
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
Collision Detection ◽  
Autonomous Vehicles ◽  
Affective Computing ◽  
Field Experiments ◽  
Road Traffic ◽  
Sensory System ◽  
Driving Mode ◽  
Human Driver

Rear-end collision detection and avoidance is one of the most crucial driving tasks of self-driving vehicles. Mathematical models and fuzzy logic-based methods have recently been proposed to improve the effectiveness of the rear-end collision detection and avoidance systems in autonomous vehicles (AVs). However, these methodologies do not tackle real-time object detection and response problems in dense/dynamic road traffic conditions due to their complex computation and decision-making structures. In our previous work, we presented an affective computing-inspired Enhanced Emotion Enabled Cognitive Agent (EEEC_Agent), which is capable of rear-end collision avoidance using artificial human driver emotions. However, the architecture of the EEEC_Agent is based on an ultrasonic sensory system which follows three-state driving strategies without considering the neighbor vehicles types. To address these issues, in this paper we propose an extended version of the EEEC_Agent which contains human driver-inspired dynamic driving mode controls for autonomous vehicles. In addition, a novel end-to-end learning-based motion planner has been devised to perceive the surrounding environment and regulate driving tasks accordingly. The real-time in-field experiments performed using a prototype AV demonstrate the effectiveness of this proposed rear-end collision avoidance system.

scholarly journals Real-time predication and navigation on traffic congestion model with equilibrium Markov chain

2018 ◽  
Vol 14 (4) ◽  
pp. 155014771876978 ◽  
Author(s):  
Yan Zheng ◽  
Yanran Li ◽  
Chung-Ming Own ◽  
Zhaopeng Meng ◽  
Mengya Gao
Keyword(s):  
Markov Chain ◽  
Real Time ◽  
Traffic Congestion ◽  
Field Experiments ◽  
Road Traffic ◽  
Markov Chain Model ◽  
Chain Model ◽  
The Road ◽  
Shortest Path Algorithms ◽  
On The Road

With the explosive growth of vehicles on the road, traffic congestion has become an inevitable problem when applying guidance algorithms to transportation networks in a busy and crowded city. In our study, the authors proposed an advanced prediction and navigation models on a dynamic traffic network. In contrast to the traditional shortest path algorithms, focused on the static network, the first part of our guiding method considered the potential traffic jams and was developed to provide the optimal driving advice for the distinct periods of a day. Accordingly, by dividing the real-time Global Positioning System data of taxis in Shenzhen city into 50 regions, the equilibrium Markov chain model was designed for dispatching vehicles and applied to ease the city congestion. With the reveals of our field experiments, the traffic congestion of city traffic networks can be alleviated effectively and efficiently, the system performance also can be retained.

REAL-TIME SELF-COLLISION AVOIDANCE SYSTEM FOR ROBOTS USING ROBE

2004 ◽  
Vol 01 (03) ◽  
pp. 533-550 ◽  
Author(s):  
FUMI SETO ◽  
KAZUHIRO KOSUGE ◽  
YASUHISA HIRATA
Keyword(s):  
Mobile Robot ◽  
Real Time ◽  
Collision Avoidance ◽  
Collision Detection ◽  
Reaction Force ◽  
Collision Avoidance System ◽  
Representation Method ◽  
Elastic Elements

In this paper, we propose a real-time self-collision avoidance system for robots which cooperate with a human/humans. First, the robot is represented by elastic elements. The representation method is referred to as RoBE (Representation of Body by Elastic elements). Elastic balls and cylinders are used as the elements to simplify collision detection, although elements of any shape could be used for RoBE. When two elements collide with each other, a reaction force is generated between them, and self-collision avoidance motion is generated by the reaction force. Experiments using the mobile robot with dual manipulators, referred to as MR Helper, illustrate the validity of the proposed system.

A Method for Searching Optimal Routes with Collision Avoidance on Raster Charts

2003 ◽  
Vol 56 (3) ◽  
pp. 371-384 ◽  
Author(s):  
Ki-Yin Chang ◽  
Gene Eu Jan ◽  
Ian Parberry
Keyword(s):  
Data Structure ◽  
Real Time ◽  
Collision Avoidance ◽  
Collision Detection ◽  
Linear Time ◽  
Routing Algorithm ◽  
Time And Space ◽  
Navigation Safety

Collision avoidance is an intensive discussion issue for navigation safety. This article introduces a new routing algorithm for finding optimal routes with collision detection and avoidance on raster charts or planes. After the required data structure of the raster chart is initialized, the maze routing algorithm is applied to obtain the particular route of each ship. Those ships that have potential to collide will be detected by simulating the particular routes with ship domains. The collision avoidance scheme can be achieved by using the collision-area-marking method with collision avoidance rules at sea. The algorithm has linear time and space complexities, and is sufficiently fast to perform real-time routing on the raster charts.

scholarly journals Using Real-Time Dynamic Prediction to Implement IoV-Based Collision Avoidance

2019 ◽  
Vol 9 (24) ◽  
pp. 5370
Author(s):  
Che-Cheng Chang ◽  
Wei-Ming Lin ◽  
Chuan-An Lai
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Collision Avoidance ◽  
Collision Detection ◽  
Confidence Region ◽  
Dynamic Prediction ◽  
Internet Of Vehicles ◽  
Time Dynamic ◽  
The Future ◽  
Future Position

For some IoV-based collision-avoidance architectures, it is not necessary that all vehicles have communication abilities. Hence, they need some particular designs and extra components. In the literature, one of them uses a camera mounted onto the infrastructure at an intersection to realize collision detection. Consequently, technologies for real-time object detection and dynamic prediction are required for the purposes of collision avoidance. In this paper, we propose an interesting method to predict the future position of a vehicle based on a well-known, real-time object detection project, YOLOv3. Our algorithm utilizes the concept of vehicle dynamics and the confidence region to predict the future position on vehicles. This will help us to realize real-time dynamic prediction and Internet of Vehicles (IoV)-based collision detection. Lastly, in accordance with the experimental results, our design shows the performance for predicting the future position of a vehicle.

CycleGuard

2021 ◽  
Vol 5 (4) ◽  
pp. 1-30
Author(s):  
Wenqiang Jin ◽  
Srinivasan Murali ◽  
Youngtak Cho ◽  
Huadi Zhu ◽  
Tianhao Li ◽  
...  
Keyword(s):  
Collision Detection ◽  
High Frequency ◽  
Field Experiments ◽  
Road Traffic ◽  
Detection System ◽  
Acoustic Signals ◽  
Proof Of Concept ◽  
Acoustic Ranging ◽  
Rich Information ◽  
Commercial Off The Shelf

Every year 41,000 cyclists die in road traffic-related incidents worldwide [47]. One of the most startling and infuriating conflicts that cyclists experience is the so-called "right hook". It refers to a vehicle striking a cyclist heading in the same direction by turning right into the cyclist. To prevent such a crash, this work presents CycleGuard, an acoustic-based collision detection system using smartphones. It is composed of a cheap commercial off-the-shelf (COTS) portable speaker that emits imperceptible high-frequency acoustic signals and a smartphone for reflected signal reception and analysis. Since received acoustic signals bear rich information of their reflecting objects, CycleGuard applies advanced acoustic ranging techniques to extract those information for traffic analysis. Cyclists are alerted if any pending right hook crashes are detected. Real-time alerts ensure that cyclists have sufficient time to react, apply brakes, and eventually avoid the hazard. To validate the efficacy of CycleGuard, we implement a proof-of-concept prototype and carry out extensive in-field experiments under a broad spectrum of settings. Results show that CycleGuard achieves up to 95% accuracy in preventing right hook crashes and is robust to various scenarios. It is also energy-friendly to run on battery-powered smartphones.

A real-time push system on road traffic information

2014 ◽  
Author(s):  
Yang Xu ◽  
Zhang Zhenjiang ◽  
Liu Yun
Keyword(s):  
Real Time ◽  
Road Traffic ◽  
Traffic Information

Autonomous Vehicles’ Car-Following Drivability Evaluation Based on Driving Behavior Spectrum Reference Model

2021 ◽  
pp. 036119812199485
Author(s):  
Xiao Qi ◽  
Ying Ni ◽  
Yiming Xu ◽  
Ye Tian ◽  
Junhua Wang ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Reference Model ◽  
Numerical Test ◽  
Safety Evaluation ◽  
Driving Behavior ◽  
Car Following ◽  
Driving Behaviors ◽  
Human Driver ◽  
Normal Human ◽  
Naturalistic Driving

A large portion of the accidents involving autonomous vehicles (AVs) are not caused by the functionality of AV, but rather because of human intervention, since AVs’ driving behavior was not properly understood by human drivers. Such misunderstanding leads to dangerous situations during interaction between AV and human-driven vehicle (HV). However, few researches considered HV-AV interaction safety in AV safety evaluation processes. One of the solutions is to let AV mimic a normal HV’s driving behavior so as to avoid misunderstanding to the most extent. Therefore, to evaluate the differences of driving behaviors between existing AV and HV is necessary. DRIVABILITY is defined in this study to characterize the similarity between AV’s driving behaviors and expected behaviors by human drivers. A driving behavior spectrum reference model built based on human drivers’ behaviors is proposed to evaluate AVs’ car-following drivability. The indicator of the desired reaction time (DRT) is proposed to characterize the car-following drivability. Relative entropy between the DRT distribution of AV and that of the entire human driver population are used to quantify the differences between driving behaviors. A human driver behavior spectrum was configured based on naturalistic driving data by human drivers collected in Shanghai, China. It is observed in the numerical test that amongst all three types of preset AVs in the well-received simulation package VTD, the brisk AV emulates a normal human driver to the most extent (ranking at 55th percentile), while the default AV and the comfortable AV rank at 35th and 8th percentile, respectively.

Safety Enhancement by Automated Driving: What are the Relevant Scenarios?

2020 ◽  
Vol 64 (1) ◽  
pp. 1686-1690
Author(s):  
Niklas Grabbe ◽  
Michael Höcher ◽  
Alexander Thanos ◽  
Klaus Bengler
Keyword(s):  
Traffic Safety ◽  
Road Traffic ◽  
Test Cases ◽  
Automated Driving ◽  
Road Traffic Safety ◽  
Human Driver ◽  
Driving Task ◽  
Promising Solution ◽  
Vehicle Automation ◽  
Safety Enhancement

Automated driving offers great possibilities in traffic safety advancement. However, evidence of safety cannot be provided by current validation methods. One promising solution to overcome the approval trap (Winner, 2015) could be the scenario-based approach. Unfortunately, this approach still results in a huge number of test cases. One possible way out is to show the current, incorrect path in the argumentation and strategy of vehicle automation, and focus on the systemic mechanisms of road traffic safety. This paper therefore argues the case for defining relevant scenarios and analysing them systemically in order to ultimately reduce the test cases. The relevant scenarios are based on the strengths and weaknesses, in terms of the driving task, for both the human driver and automation. Finally, scenarios as criteria for exclusion are being proposed in order to systemically assess the contribution of the human driver and automation to road safety.

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