Automated Driving Simulation Platform Design on Collision Avoidance Decision Making for Vulnerable Road Users

2021 ◽  
pp. 787-791
Author(s):  
Xiang Si ◽  
Quan Yuan
Keyword(s):  
Decision Making ◽  
Collision Avoidance ◽  
Driving Simulation ◽  
Simulation Platform ◽  
Automated Driving ◽  
Platform Design ◽  
Road Users ◽  
Vulnerable Road Users

scholarly journals Socially Acceptable Collision Avoidance System for Vulnerable Road Users

2016 ◽  
Vol 49 (3) ◽  
pp. 436-441 ◽  
Author(s):  
Mümin Tolga Emirler ◽  
Haoan Wang ◽  
Bilin Aksun Güvenç
Keyword(s):  
Collision Avoidance ◽  
Road Users ◽  
Vulnerable Road Users ◽  
Collision Avoidance System

A novel adaptive lane change method in transient dynamic traffic conditions for highly automated vehicles

2021 ◽  
pp. 146441932110248
Author(s):  
Mohsen Rafat ◽  
Shahram Azadi
Keyword(s):  
Decision Making ◽  
Collision Avoidance ◽  
Polynomial Equation ◽  
Optimization Method ◽  
Lane Change ◽  
Dynamic Traffic ◽  
Automated Driving ◽  
Transient Dynamic ◽  
Traffic Conditions ◽  
Decision Making Unit

Trajectory planning with consideration of surrounding vehicles and decision-making in the middle of complicated manoeuvres are some of the most important challenges regarding the implementation of automated driving. Since transient dynamic traffic conditions was limited to the start of the manoeuvre in previous research, no solution was provided for the surrounding vehicles’ transient changes during the manoeuvre. The algorithm presented in this paper is able to adapt to unstable variable traffic conditions and it is robust to changes in the surrounding vehicles’ conditions, even during the lane change manoeuvre. The Adaptive Lane Change algorithm provides all possible safe trajectories for any moment of manoeuvre via applying DE optimization method on a fifth-order polynomial equation. In this way, it is able to make a new decision and plan safe trajectories according to the new conditions of surrounding vehicles during the manoeuvre. Also, it guarantees collision avoidance at all-time via simultaneous longitudinal and lateral vehicle control. Improving the trajectory during a lane change manoeuvre regarding the surrounding vehicles’ conditions is considered as one of the main contributions of the presented algorithm. A second main contribution is the collision avoidance considering the vehicle’s dynamic via returning to the initial lane when there is no safe trajectory in the target lane, even during the lane change manoeuvre. The decision-making unit is evaluated by real driving tests. Then, the whole structure is simulated with MATLAB in complex transient dynamic traffic conditions via various scenarios and its performance is tested with IPG CarMaker in the presence of simulated surrounding vehicles.

scholarly journals Generation of Pedestrian Crossing Scenarios Using Ped-Cross Generative Adversarial Network

2021 ◽  
Vol 11 (2) ◽  
pp. 471
Author(s):  
James Spooner ◽  
Vasile Palade ◽  
Madeline Cheah ◽  
Stratis Kanarachos ◽  
Alireza Daneshkhah
Keyword(s):  
Autonomous Vehicles ◽  
Generative Adversarial Networks ◽  
Automated Driving ◽  
Real World Data ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Road Users ◽  
Vulnerable Road Users ◽  
Human Pose ◽  
Pedestrian Crossing

The safety of vulnerable road users is of paramount importance as transport moves towards fully automated driving. The richness of real-world data required for testing autonomous vehicles is limited and furthermore, available data do not present a fair representation of different scenarios and rare events. Before deploying autonomous vehicles publicly, their abilities must reach a safety threshold, not least with regards to vulnerable road users, such as pedestrians. In this paper, we present a novel Generative Adversarial Networks named the Ped-Cross GAN. Ped-Cross GAN is able to generate crossing sequences of pedestrians in the form of human pose sequences. The Ped-Cross GAN is trained with the Pedestrian Scenario dataset. The novel Pedestrian Scenario dataset, derived from existing datasets, enables training on richer pedestrian scenarios. We demonstrate an example of its use through training and testing the Ped-Cross GAN. The results show that the Ped-Cross GAN is able to generate new crossing scenarios that are of the same distribution from those contained in the Pedestrian Scenario dataset. Having a method with these capabilities is important for the future of transport, as it will allow for the adequate testing of Connected and Autonomous Vehicles on how they correctly perceive the intention of pedestrians crossing the street, ultimately leading to fewer pedestrian casualties on our roads.

scholarly journals Negotiation and Decision-Making for a Pedestrian Roadway Crossing: A Literature Review

2019 ◽  
Vol 11 (23) ◽  
pp. 6713 ◽  
Author(s):  
Roja Ezzati Amini ◽  
Christos Katrakazas ◽  
Constantinos Antoniou
Keyword(s):  
Decision Making ◽  
Holistic Approach ◽  
Automated Driving ◽  
The Road ◽  
Communication Techniques ◽  
Road Users ◽  
Interaction Modeling ◽  
Pedestrian Crossing ◽  
Near Future ◽  
Road Crossing

The interaction among pedestrians and human drivers is a complicated process, in which road users have to communicate their intentions, as well as understand and anticipate the actions of users in their vicinity. However, road users still ought to have a proper interpretation of each others’ behaviors, when approaching and crossing the road. Pedestrians, as one of the interactive agents, demonstrate different behaviors at road crossings, which do not follow a consistent pattern and may vary from one situation to another. The presented inconsistency and unpredictability of pedestrian road crossing behaviors may thus become a challenge for the design of emerging technologies in the near future, such as automated driving system (ADS). As a result, the current paper aims at understanding the effectual communication techniques, as well as the factors influencing pedestrian negotiation and decision-making process. After reviewing the state-of-the-art and identifying research gaps with regards to vehicle–pedestrian crossing encounters, a holistic approach for road crossing interaction modeling is presented and discussed. It is envisioned that the presented holistic approach will result in enhanced safety, sustainability, and effectiveness of pedestrian road crossings.

scholarly journals Analysis of Pedestrian Street-Crossing Decision-Making Based on Vehicle Deceleration-Safety Gap

2020 ◽  
Vol 17 (24) ◽  
pp. 9247
Author(s):  
Hongjia Zhang ◽  
Yingshi Guo ◽  
Yunxing Chen ◽  
Qinyu Sun ◽  
Chang Wang
Keyword(s):  
Decision Making ◽  
Autonomous Vehicles ◽  
Traffic Crashes ◽  
Motion Data ◽  
Street Crossing ◽  
Road Users ◽  
Vulnerable Road Users ◽  
Decision Making Model ◽  
Pedestrian Street ◽  
Vehicle Deceleration

Numerous traffic crashes occur every year on zebra crossings in China. Pedestrians are vulnerable road users who are usually injured severely or fatally during human-vehicle collisions. The development of an effective pedestrian street-crossing decision-making model is essential to improving pedestrian street-crossing safety. For this purpose, this paper carried out a naturalistic field experiment to collect a large number of vehicle and pedestrian motion data. Through interviewed with many pedestrians, it is found that they pay more attention to whether the driver can safely brake the vehicle before reaching the zebra crossing. Therefore, this work established a novel decision-making model based on the vehicle deceleration-safety gap (VD-SGM). The deceleration threshold of VD-SGM was determined based on signal detection theory (SDT). To verify the performance of VD-SGM proposed in this work, the model was compared with the Raff model. The results show that the VD-SGM performs better and the false alarm rate is lower. The VD-SGM proposed in this work is of great significance to improve pedestrians’ safety. Meanwhile, the model can also increase the efficiency of autonomous vehicles.

scholarly journals A Systematic Review of Virtual Reality Applications for Automated Driving: 2009–2020

2021 ◽  
Vol 3 ◽  
Author(s):  
Andreas Riegler ◽  
Andreas Riener ◽  
Clemens Holzmann
Keyword(s):  
Virtual Reality ◽  
Future Research ◽  
Automated Driving ◽  
Specific Level ◽  
Road Users ◽  
Wide Range ◽  
Scientific Papers ◽  
The Status ◽  
Vulnerable Road Users ◽  
Vehicle Automation

While virtual reality (VR) interfaces have been researched extensively over the last decades, studies on their application in vehicles have only recently advanced. In this paper, we systematically review 12 years of VR research in the context of automated driving (AD), from 2009 to 2020. Due to the multitude of possibilities for studies with regard to VR technology, at present, the pool of findings is heterogeneous and non-transparent. We investigated N = 176 scientific papers of relevant journals and conferences with the goal to analyze the status quo of existing VR studies in AD, and to classify the related literature into application areas. We provide insights into the utilization of VR technology which is applicable at specific level of vehicle automation and for different users (drivers, passengers, pedestrians) and tasks. Results show that most studies focused on designing automotive experiences in VR, safety aspects, and vulnerable road users. Trust, simulator and motion sickness, and external human-machine interfaces (eHMIs) also marked a significant portion of the published papers, however a wide range of different parameters was investigated by researchers. Finally, we discuss a set of open challenges, and give recommendation for future research in automated driving at the VR side of the reality-virtuality continuum.

scholarly journals PLANNING AND COORDINATION IN DRIVING SIMULATION

2015 ◽  
Vol 2 (2) ◽  
pp. 40-44 ◽  
Author(s):  
Martin Schaefer
Keyword(s):  
Driving Simulator ◽  
Driving Simulation ◽  
Coordination Mechanisms ◽  
Simulation Platform ◽  
Automated Driving ◽  
Traffic Generation ◽  
Coordination Methods

We present an overview of coordination and planning tasks that we face with during the development of the AgentDrive simulation platform. We particularly describe an integration of the AgentDrive with a driving simulator OpenDS. We demonstrate how the planning and coordination mechanisms can be applied in a driving simulator for automated driving applications or realistic traffic generation. We emphasize particular planning and/or coordination methods that were already developed using AgentDrive platform.

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