Braking Strategy for an Autonomous Vehicle in a Mixed Traffic Scenario

The introduction of autonomous vehicles in the surface transportation system could improve traffic safety and reduce traffic congestion and negative environmental effects. Although the continuous evolution in computing, sensing, and communication technologies can improve the performance of autonomous vehicles, the new combination of autonomous automotive and electronic communication technologies will present new challenges, such as interaction with other nonautonomous vehicles, which must be addressed before implementation. The objective of this study was to identify the risks associated with the failure of an autonomous vehicle in mixed traffic streams. To identify the risks, the autonomous vehicle system was first disassembled into vehicular components and transportation infrastructure components, and then a fault tree model was developed for each system. The failure probabilities of each component were estimated by reviewing the published literature and publicly available data sources. This analysis resulted in a failure probability of about 14% resulting from a sequential failure of the autonomous vehicular components alone in the vehicle’s lifetime, particularly the components responsible for automation. After the failure probability of autonomous vehicle components was combined with the failure probability of transportation infrastructure components, an overall failure probability related to vehicular or infrastructure components was found: 158 per 1 million mi of travel. The most critical combination of events that could lead to failure of autonomous vehicles, known as minimal cut-sets, was also identified. Finally, the results of fault tree analysis were compared with real-world data available from the California Department of Motor Vehicles autonomous vehicle testing records.

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Trip Cost Estimation of Connected Autonomous Vehicle Mixed Traffic Flow in a Two-Route Traffic Network

Journal of Advanced Transportation ◽

10.1155/2020/8884732 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Zhizhen Liu ◽

Hong Chen ◽

Hengrui Chen ◽

Xiaoke Sun ◽

Qi Zhang

Keyword(s):

Bounded Rationality ◽

Traffic Flow ◽

Cost Estimation ◽

Penetration Rate ◽

Autonomous Vehicle ◽

Flow Simulation ◽

Mixed Traffic ◽

Traffic Condition ◽

Mixed Traffic Flow ◽

Traffic Flow Simulation

With the advancement of connected autonomous vehicle (CAV) technology, research on future traffic conditions after the popularization of CAVs needs to be resolved urgently. Bounded rationality of human drivers is essential for simulating traffic flow precisely, but few studies focus on the traffic flow simulation considered bounded rationality in CAV mixed traffic flow. In this study, we introduce random bounded rationality into the hybrid feedback strategy (HFS) under CAV mixed traffic flow to explore the impacts of CAV penetration rate on the trip cost of vehicles. First, we investigated the bounded rationality of drivers, and we found that it follows normal contribution. Then, we proposed HFS considering random bounded rationality and the CAV penetration rate to simulate the traffic condition. The numerical results show that the enhancement of the CAV penetration rate could reduce total trip cost. The research could help us to simulate the CAVs mixed traffic flow more precisely and realistically.

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Impact of Autonomous-Vehicle-Only Lanes in Mixed Traffic Conditions

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119847475 ◽

2019 ◽

Vol 2673 (9) ◽

pp. 430-439 ◽

Cited By ~ 4

Author(s):

Hwapyeong Yu ◽

Sehyun Tak ◽

Minju Park ◽

Hwasoo Yeo

Keyword(s):

Autonomous Vehicles ◽

Road Traffic ◽

Positive Impact ◽

Penetration Rate ◽

Autonomous Vehicle ◽

Market Penetration ◽

Mixed Traffic ◽

Negative Effects ◽

Average Speed ◽

Time To Collision

The introduction of autonomous vehicles (AVs) in the near future will have a significant impact on road traffic. AVs may have advantages in efficiency and convenience, but safety can be compromised in mixed operations of manual vehicles and AVs. To deal with the issues associated with mixed traffic and to avoid its negative effects, a special purpose lane reserved for AVs can be proposed to segregate AVs from manual vehicles. In this research, we analyze the effect on efficiency and safety of AVs in mixed traffic and in a situation where an AV-only lane is deployed. In the analysis, we investigate the average speed, the throughput, and the inverse time-to-collision (ITTC). We differentiate the behaviors of manual vehicles and AVs through the reaction time, desired speed, and car-following models. As a result, we observe that the efficiency is improved when the market penetration rate of AVs increases, especially when the highway throughput increases by up to 84% in the case of mixed traffic. However, safety worsens when the market penetration of AVs is under 40%. In this case, the average speed can be improved and the frequency of dangerous situations (ITTC > 0.49) can be reduced drastically in the merging section by making the innermost lane AV-only. Accordingly, we conclude that AV-only lanes can have a significant positive impact on efficiency and safety when the market penetration rate of AVs is low.

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Adverse Effect of Congestion on Economy, Health and Environment Under Mixed Traffic Scenario

Transportation in Developing Economies ◽

10.1007/s40890-021-00125-4 ◽

2021 ◽

Vol 7 (2) ◽

Author(s):

Satya Ranjan Samal ◽

Malaya Mohanty ◽

S. Moses Santhakumar

Keyword(s):

Adverse Effect ◽

Mixed Traffic ◽

Traffic Scenario

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Vision-only fully automated driving in dynamic mixed-traffic scenarios

it - Information Technology ◽

10.1515/itit-2015-0005 ◽

2015 ◽

Vol 57 (4) ◽

Cited By ~ 3

Author(s):

Ulrich Schwesinger ◽

Pietro Versari ◽

Alberto Broggi ◽

Roland Siegwart

Keyword(s):

Motion Planning ◽

Real World ◽

Autonomous Vehicle ◽

Wide Angle ◽

Mixed Traffic ◽

Local Motion ◽

Automated Driving ◽

Fully Automated Driving

AbstractIn this work an overview of the local motion planning and dynamic perception framework within the V-Charge project is presented. This framework enables the V-Charge car to autonomously navigate in dynamic mixed-traffic scenarios. Other traffic participants are detected, classified and tracked from a combination of stereo and wide-angle monocular cameras. Predictions of their future movements are generated utilizing infrastructure information. Safe motion plans are acquired with a system-compliant sampling-based local motion planner. We show the navigation performance of this vision-only autonomous vehicle in both simulation and real-world experiments.

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