Planning and Decision-making for Connected Autonomous Vehicles at Road Intersections: A Review

Shen, Li; Shu, Keqi; Chen, Chaoyi; Cao, Dongpu

doi:10.1186/s10033-021-00639-3

Cited by 45 publications

(20 citation statements)

References 170 publications

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“…The state Equation ( 3) is substituted in (8) to achieve this while eliminating state constraints that would worsen the mathematical model of the OCP, and after some rearrangement, this results in the state-dependent controlling constraint as in Equation (9).…”

Section: System Model and Its Parametersmentioning

confidence: 99%

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HOPAV: Hybrid optimization‐oriented path planning for non‐connected and connected automated vehicles

Arumugam¹,

Kavitha

Prado

et al. 2023

IET Control Theory & Appl

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Over the past ten years, autonomous driving has garnered a great deal of interest from both the scientific community and business. Strong technological advancements have made automated driving more practical because human driving abilities seem limited in terms of driving experience, reaction time, and the effectiveness of real‐time decisions. The development of highly autonomous driving algorithms is inextricably tied to planning and changing a vehicle path that must be user‐acceptable, efficient, and collision‐free. Path planning for road vehicles is a difficult problem due to the high speed involved and the requirement to assure passenger safety. Here, a new path‐planning method is developed for both connected and disconnected automatic road vehicles on multilane highways. This paradigm states that the right phrases to describe the objectives of vehicle improvement, passenger comfort, prevention of vehicle‐to‐vehicle collisions and road deviations are included in the objective function. Hunger Games improved Archimedes optimization (HGE‐ARCO) is used to optimize the paths for achieving better‐planned outcomes. At the 100th penetration rate, the HGE‐ARCO scheme reached a top speed of about 99 km/h. The results shows unmistakably that the proposed HGE‐ARCO produces a time of 12.3021 s, which is less than other conventional methods.

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Section: System Model and Its Parametersmentioning

confidence: 99%

“…Even if geometric approaches offer certain benefits, they cannot make up for their drawbacks. These path-planning techniques need to have their intelligence and adaptability improved in complicated dynamic contexts [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

HOPAV: Hybrid optimization‐oriented path planning for non‐connected and connected automated vehicles

Arumugam¹,

Kavitha

Prado

et al. 2023

IET Control Theory & Appl

View full text Add to dashboard Cite

show abstract

“…However, some of the limitations of these approaches are that they do not deal with the input uncertainties and cannot generalize to unknown situations, which makes them difficult to scale them to the complexity of real-world driving where the events may be unprecedented and unknown. Furthermore, when dealing with complex urban environments, such as road intersections, where various uncertainties exist, a rule-based approach cannot maintain safe and efficient driving [ 10 , 15 , 32 , 33 , 34 ].…”

Section: The Analyses Of Decision-making Relevant Solutions For Auton...mentioning

confidence: 99%

“…Katrakazas et al [ 5 ] presented a review of the fusion of solution approaches for planning and decision-making. Li et al [ 10 ] mainly overviewed the research studies conducted on the planning and decision-making technologies at intersections. Leon et al [ 11 ] conducted a review on tracking, prediction and decision-making approaches for AD.…”

Section: Introductionmentioning

confidence: 99%

How Do Autonomous Vehicles Decide?

Malik

Khan

El-Sayed

et al. 2022

Sensors

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The advancement in sensor technologies, mobile network technologies, and artificial intelligence has pushed the boundaries of different verticals, e.g., eHealth and autonomous driving. Statistics show that more than one million people are killed in traffic accidents yearly, where the vast majority of the accidents are caused by human negligence. Higher-level autonomous driving has great potential to enhance road safety and traffic efficiency. One of the most crucial links to building an autonomous system is the task of decision-making. The ability of a vehicle to make robust decisions on its own by anticipating and evaluating future outcomes is what makes it intelligent. Planning and decision-making technology in autonomous driving becomes even more challenging, due to the diversity of the dynamic environments the vehicle operates in, the uncertainty in the sensor information, and the complex interaction with other road participants. A significant amount of research has been carried out toward deploying autonomous vehicles to solve plenty of issues, however, how to deal with the high-level decision-making in a complex, uncertain, and urban environment is a comparatively less explored area. This paper provides an analysis of decision-making solutions approaches for autonomous driving. Various categories of approaches are analyzed with a comparison to classical decision-making approaches. Following, a crucial range of research gaps and open challenges have been highlighted that need to be addressed before higher-level autonomous vehicles hit the roads. We believe this survey will contribute to the research of decision-making methods for autonomous vehicles in the future by equipping the researchers with an overview of decision-making technology, its potential solution approaches, and challenges.

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“…One of the solutions is So Simulation of Urban Mobility where all the data of vehicles are managed by the database. Any vehicles where the database recorded the priority will be given preference (Li et al, 2021). This prioritized vehicle will increase the speed up while other vehicles will provide him the way.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Four-Way Crossroad Safety Management For Autonomous, Non-Autonomous and VIP Vehicles

Naeem

Soomro

Bhuva

et al. 2023

2023 IEEE International Conference on Emerging Trends in Engineering, Sciences and Technology (ICES&T)

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In the current era, autonomous vehicle and non-autonomous vehicle are transforming and adopting intelligent road management techniques to communicating with vehicles so that the tra c system may become more e cient. Intersection management unit technique used to handle and control all types of vehicles. VIP vehicle enter our road so it should disturb all routes, but IM unit handles all vehicles with e cient way by using preemptive scheduling and non-preemptive scheduling technique. According to our scenario, we are using Nishtar road, m4 motorway, M5 Motorway and Washington road. Simulation result show that Washington road route handle all types of vehicles better way as compared to all other routes. Washington road time delay for 50 all types of vehicles is 3.62 sec for autonomous vehicle for and non-autonomous vehicles delay time is 4.33 sec. Evolution of V2XCommunication and Integration of Blockchain for Security Enhancements Vehicle-to-every thigs communication Light weight message veri cation scheme for vehicle to everything (V2X) communication The proposed scheme is e cient for vanet and its security Comparing the suggested protocols ndings to those of other pertinent communication techniques, it is found that it consumes less computing power and also secure communication than other vehicular communication schemes.

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Planning and Decision-making for Connected Autonomous Vehicles at Road Intersections: A Review

Cited by 45 publications

References 170 publications

HOPAV: Hybrid optimization‐oriented path planning for non‐connected and connected automated vehicles

HOPAV: Hybrid optimization‐oriented path planning for non‐connected and connected automated vehicles

How Do Autonomous Vehicles Decide?

Intelligent Four-Way Crossroad Safety Management For Autonomous, Non-Autonomous and VIP Vehicles

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