Safety Evaluation for Connected and Autonomous Vehicles’ Exclusive Lanes considering Penetrate Ratios and Impact of Trucks Using Surrogate Safety Measures

Zhang, Jian; Wu, Kunrun; Cheng, Min; Yang, Min; Cheng, Yang; Li, Shen

doi:10.1155/2020/5847814

Cited by 28 publications

(36 citation statements)

References 47 publications

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“…Surrogate safety measures are widely used to assess the potential risk of traffic crashes ( 33 ). In this case, Time-to-Collision (TTC) is one of the most popular metrics utilized to measure the safety of two individual vehicles for online and offline evaluation when conducting simulations or to analyze naturalistic driving data ( 34, 35 ). The TTC notion for vehicle i is described as:…”

Section: Methodsmentioning

confidence: 99%

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Navigating Electric Vehicles Along a Signalized Corridor via Reinforcement Learning: Toward Adaptive Eco-Driving Control

Zhang

Jiang

Cui

et al. 2022

Transportation Research Record

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One problem associated with the operation of electric vehicles (EVs) is the limited battery, which cannot guarantee their endurance. The increasing electricity consumption will also impose a burden on economy and ecology of the vehicles. To achieve energy saving, this paper proposes an adaptive eco-driving method in the environment of signalized corridors. The framework with adaptive and real-time control is implemented by the reinforcement learning technique. First, the operation of EVs in the proximity of intersections is defined as a Markov Decision Process (MDP) to apply the twin delayed deep deterministic policy gradient (TD3) algorithm, to deal with the decision process with continuous action space. Therefore, the speed of the vehicle can be adjusted continuously. Second, safety, traffic mobility, energy consumption, and comfort are all considered by designing a comprehensive reward function for the MDP. Third, the simulation study takes Aoti Street in Nanjing City with several consecutive signalized intersections as the research road network, and the state representation in MDP considers the information from consecutive downstream traffic signals. After the parameter tuning procedure, simulations are carried out for three typical eco-driving scenarios, including free flow, car following, and congestion flow. By comparing with default car-following behavior in the simulation platform SUMO and several state-of-the-art deep reinforcement learning algorithms, the proposed strategy shows a balanced and stable performance.

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Section: Methodsmentioning

confidence: 99%

“…Hayward pointed out that 1 s would be a proper threshold to serve as the measurement for a near miss ( 36 ). To further ensure safety in CAV-involved environment, the value with 2 s is used in several studies ( 34, 37 , 38 ). As a result, we define this part of the reward as:…”

Section: Methodsmentioning

confidence: 99%

Navigating Electric Vehicles Along a Signalized Corridor via Reinforcement Learning: Toward Adaptive Eco-Driving Control

Zhang

Jiang

Cui

et al. 2022

Transportation Research Record

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“…e results revealed that traffic conflicts could be reduced by up to 90-94% with a full AuV traffic stream. e safety implications of exclusive AuV lanes were identified by Zhang et al [44] with different market shares and for multiple traffic demand scenarios. is study indicated that a higher number of lanes would be required at high-demand scenarios to attain significant safety improvements.…”

Section: Literature Reviewmentioning

confidence: 99%

Multilane Microscopic Modeling to Measure Mobility and Safety Consequences of Mixed Traffic in Freeway Weaving Sections

Seraj

Qiu

2021

Journal of Advanced Transportation

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Weaving sections are components of highway networks that introduce a heightened likelihood for bottlenecks and collisions. Automated vehicle technology could address this as it holds considerable promise for transportation mobility and safety improvements. However, the implications of combining automated vehicles (AuVs) with traditional human-driven vehicles (HuVs) in weaving freeway sections have not been quantitatively measured. To address this gap, this paper objectively experimented with bidirectional (i.e., longitudinal and lateral) motion dynamics in a microscopic modeling framework to measure the mobility and safety implications for mixed traffic movement in a freeway weaving section. Our research begins by establishing a multilane microscopic model for studied vehicle types (i.e., AuV and HuV) from model predictive control with the provision to form a CACC platoon of AuV vehicles. The proposed modeling framework was tested first with HuV only on a two-lane weaving section and validated using standardized macroscopic parameters from the Highway Capacity Manual. This model was then applied to incrementally expand the AuV share for varying inflow rates of traffic. Simulation results showed that the maximum flow rate through the weaving section was attained at a 65% AuV share. At the same time, steadiness in the average speed of traffic was experienced with increasing AuV share. The results also revealed that a 95% AuV share could reduce potential conflicts by 94.28%. Finally, the results of simulated scenarios were consolidated and scaled to report expected mobility and safety outcomes from the prevailing traffic state and the optimal AuV share for the current inflow rate in weaving sections.

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“…Os autores observaram que a maioria de conflitos dos AVs acontecem em intersec ¸ões, e concluem que dependendo do grau de penetrac ¸ão dos AVs, a taxa de conflitos pode diminuir [Virdi et al, 2019]. Zhang et al avaliam a seguranc ¸a de veículos autônomos conectados, analisando a troca e a exclusividade de faixas através da ferramenta de simulac ¸ão PTV-VISSIM [Zhang et al, 2020]. Por outra parte, Alghodhaifi e Lakshmanan analisam SSMs como base para um sistema de protec ¸ão para pedestres, através de simulac ¸ões em Matlab/Simulink [Alghodhaifi e Lakshmanan, 2020].…”

Section: Trabalhos Relacionadosunclassified

Análise de Métricas Telemáticas para Auditoria de Veículos Autônomos

Ortiz

Sammarco²,

Detyniecki³

et al. 2021

Anais Do v Workshop De Computação Urbana (CoUrb 2021)

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Veículos autônomos (AVs) usam sistemas de sensoriamento externo para obter informações sobre os diversos objetos presentes ao seu redor e assim tomar decisões de condução de forma automatizada. De fato, as informações obtidas através da telemática veicular servem para monitorar o funcionamento e a forma de condução de quaisquer veículos automotores, não apenas dos AVs. De acordo com a literatura, diferentes métricas são usadas por aplicativos de asseguradoras para avaliar situações de risco potencial e como os motoristas dirigem. Quanto a AVs, porém, a avaliação destas métricas ainda é incipiente, dado que a maioria destes veículos está em desenvolvimento. Assim, com o objetivo de avaliar e quantificar a gravidade de eventos de risco no trânsito envolvendo AVs e outros usuários viários, este trabalho estuda métricas de segurança substitutas baseado em leituras de sensores exteroceptivos e proprioceptivos contidas em conjuntos de dados de AVs. Os resultados indicam que ao menos 10% dos eventos representam algum nível risco para o AV. Estes resultados ajudam a explicar o comportamento e a tomada de decisões no sistema autônomo.

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Safety Evaluation for Connected and Autonomous Vehicles’ Exclusive Lanes considering Penetrate Ratios and Impact of Trucks Using Surrogate Safety Measures

Cited by 28 publications

References 47 publications

Navigating Electric Vehicles Along a Signalized Corridor via Reinforcement Learning: Toward Adaptive Eco-Driving Control

Navigating Electric Vehicles Along a Signalized Corridor via Reinforcement Learning: Toward Adaptive Eco-Driving Control

Multilane Microscopic Modeling to Measure Mobility and Safety Consequences of Mixed Traffic in Freeway Weaving Sections

Análise de Métricas Telemáticas para Auditoria de Veículos Autônomos

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