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 Autonomous Vehicles and Vulnerable Road-Users—Important Considerations and Requirements Based on Crash Data from Two Countries

2021 ◽  
Vol 11 (7) ◽  
pp. 101
Author(s):  
Andrew Paul Morris ◽  
Narelle Haworth ◽  
Ashleigh Filtness ◽  
Daryl-Palma Asongu Nguatem ◽  
Laurie Brown ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Red Light ◽  
Road User ◽  
Red Light Running ◽  
Crash Data ◽  
Passenger Vehicles ◽  
Road Users ◽  
Vulnerable Road User ◽  
Vulnerable Road Users ◽  
Road Intersections

(1) Background: Passenger vehicles equipped with advanced driver-assistance system (ADAS) functionalities are becoming more prevalent within vehicle fleets. However, the full effects of offering such systems, which may allow for drivers to become less than 100% engaged with the task of driving, may have detrimental impacts on other road-users, particularly vulnerable road-users, for a variety of reasons. (2) Crash data were analysed in two countries (Great Britain and Australia) to examine some challenging traffic scenarios that are prevalent in both countries and represent scenarios in which future connected and autonomous vehicles may be challenged in terms of safe manoeuvring. (3) Road intersections are currently very common locations for vulnerable road-user accidents; traffic flows and road-user behaviours at intersections can be unpredictable, with many vehicles behaving inconsistently (e.g., red-light running and failure to stop or give way), and many vulnerable road-users taking unforeseen risks. (4) Conclusions: The challenges of unpredictable vulnerable road-user behaviour at intersections (including road-users violating traffic or safe-crossing signals, or taking other risks) combined with the lack of knowledge of CAV responses to intersection rules, could be problematic. This could be further compounded by changes to nonverbal communication that currently exist between road-users, which could become more challenging once CAVs become more widespread.

OPTIMISING ROAD LIGHTING TO REDUCE ROAD TRAFFIC CRASHES

2021 ◽  
Author(s):  
C.J. Robbins ◽  
S. Fotios ◽  
J. Uttley ◽  
R. Rowe
Keyword(s):  
High Speed ◽  
Road Traffic ◽  
Cost Benefit ◽  
Light Level ◽  
Traffic Crashes ◽  
Road Traffic Crashes ◽  
Road Lighting ◽  
Road Users ◽  
Ambient Light Level ◽  
Vulnerable Road Users

Pedestrians and motorcyclists are vulnerable road users, being over represented in road traffic collisions (RTCs). One assumed benefit of road lighting is a reduction in RTCs after dark by countering the impairment to the visual detection of hazards that occur after dark. One way to optimise the use of road lighting is to light only those sections of road where light level, and hence visibility, is an important factor. The current study used change in ambient light level on RTCs to investigate those situations where improved vision is likely to have significant impact, and therefore the situations where road lighting is of better cost-benefit effectiveness. For both motorcyclist and pedestrian RTCs there was a significant increase in overall RTC risk in darkness compared to daylight, indicating that there may be an overall benefit of road lighting. While darkness was a particular detriment at junctions for motorcyclists and on high-speed roads for pedestrians, road lighting may not be effective mitigation in either case and therefore alternative ways of increasing conspicuity should be considered.

Implementation of Decision-Making Model System for Autonomous Vehicles in Virtual Environment

2006 ◽  
Vol 532-533 ◽  
pp. 1128-1131
Author(s):  
Yan Fei Liang ◽  
Han Wu He ◽  
De Tao Zheng ◽  
Xin Chen
Keyword(s):  
Decision Making ◽  
Virtual Environment ◽  
Autonomous Vehicles ◽  
Model System ◽  
Virtual Reality Environment ◽  
Environment Modeling ◽  
Driving Simulators ◽  
Modeling Technology ◽  
Virtual Vehicle ◽  
Decision Making Model

This paper established the framework of the decision-making model system for autonomous vehicles. Based on virtual reality environment modeling technology, the virtual scene was obtained. The driving performance of autonomous vehicles in real environment was simulated with that of the virtual vehicle in virtual environment. It was studied the influence of driver’s aggressiveness on lane-changed performance through considering human factors, and several longitudinal driving modes were classified and discussed. Three-power B spline function was used in this paper to plan path by interpolating characteristics points. The driving framework and the driving models described in this paper serve to address the problem of building more realistic traffic at the microscopic level in driving simulators. The autonomous vehicles based on this system can be used as the vehicles in simulators and help to design traffic or help to verify the performance of vehicles.

scholarly journals A Security-by-Design Decision-Making Model for Risk Management in Autonomous Vehicles

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Mohamed Abdel-Basset ◽  
Abduallah Gamal ◽  
Nour Moustafa ◽  
Ahmed Abdel-Monem ◽  
Nissreen El-Saber
Keyword(s):  
Decision Making ◽  
Risk Management ◽  
Autonomous Vehicles ◽  
Design Decision ◽  
Security By Design ◽  
Decision Making Model

scholarly journals Human-like Interactive Behavior Generation for Autonomous Vehicles: A Bayesian Game-theoretic Approach with Turing Test

2021 ◽  
Author(s):  
Yiran Zhang ◽  
Peng Hang ◽  
Chao Huang ◽  
Chen Lv
Keyword(s):  
Decision Making ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Turing Test ◽  
Theoretic Model ◽  
Theoretic Approach ◽  
Bayesian Game ◽  
Vehicle Simulation ◽  
Road Users ◽  
Game Theoretic

Interacting with surrounding road users is a key feature of vehicles and is critical for intelligence testing of autonomous vehicles. The Existing interaction modalities in autonomous vehicle simulation and testing are not sufficiently smart and can hardly reflect human-like behaviors in real world driving scenarios. To further improve the technology, in this work we present a novel hierarchical game-theoretical framework to represent naturalistic multi-modal interactions among road users in simulation and testing, which is then validated by the Turing test. Given that human drivers have no access to the complete information of the surrounding road users, the Bayesian game theory is utilized to model the decision-making process. Then, a probing behavior is generated by the proposed game theoretic model, and is further applied to control the vehicle via Markov chain. To validate the feasibility and effectiveness, the proposed method is tested through a series of experiments and compared with existing approaches. In addition, Turing tests are conducted to quantify the human-likeness of the proposed algorithm. The experiment results show that the proposed Bayesian game theoretic framework can effectively generate representative scenes of human-like decision-making during autonomous vehicle interactions, demonstrating its feasibility and effectiveness. Corresponding author(s) Email:   [email protected]  

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