Decision Making for Autonomous Vehicles' Strategy in Triple-Lane Roundabout Intersections

Samizadeh, Shadi; Nikoofard, Amirhossein; Yektamoghadam, Hossein

doi:10.1109/iccia54998.2022.9737190

Cited by 3 publications

(2 citation statements)

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“…Comparing between two methods shows the output of MPC tracking has been satisfactory. Samizadeh et al (2022) apply a new algorithm to choose the least waiting time lane. This paper tries to converge the best lane for more efficiency and compare the results on working days and holidays.…”

Section: Related Workmentioning

confidence: 99%

Multi‐criteria evolutionary optimization of a traffic light using genetics algorithm and teaching‐learning based optimization

Yektamoghadam,

Nikoofard,

Behzadi

et al. 2023

Expert Systems

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Today, the development of urbanization and increasing the number of vehicles has resulted in displeased consequences like traffic congestion and vehicle queuing. The vast majority of countries in the world encounter the challenge of the explosive rise in traffic demand. In this regard, it is necessary to meet traffic demand in transport networks, especially in metropolitans. In traffic management and shortening the trip duration, traffic lights on the signalized intersections play an essential role in urban pathways. This work provides a multi‐criteria decision‐making method for optimum traffic light control in an isolated corner. The main idea involves establishing a set of sub‐optimal solutions for traffic light timing and selecting the best one among the diverse solutions. We have mathematically modelled the problem as an optimization problem to achieve an optimal solution with less waiting time for vehicles in intersections and the lowest cost. Genetic algorithm (GA) and Teaching‐Learning‐based Optimization (TLBO) are utilized for each phase to create a set of suitable timing scenarios. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used to identify the best scenario, considering both waiting vehicles and traffic capacity as decision criteria. Its efficiency has been demonstrated over three different traffic volumes. Also, in a real‐world implementation, its practical capability has been approved at a crossroads in Mashhad, Iran. The simulations indicate the improvement in the number of vehicles waiting behind the crossroad and the traffic capacity by 10% and 6.76% compared to the existing signal timing of the studied intersection, respectively.

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Section: Related Workmentioning

confidence: 99%

Multi‐criteria evolutionary optimization of a traffic light using genetics algorithm and teaching‐learning based optimization

Yektamoghadam,

Nikoofard,

Behzadi

et al. 2023

Expert Systems

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“…It is anticipated that autonomous vehicles will gradually replace conventional vehicles in the future. In addition to performing traditional vehicle functions, such as acceleration, deceleration, braking, and motion control, autonomous vehicles also exhibit AI-driven behaviors including environment perception, task planning, path planning, vehicle control mechanisms, and intelligent obstacle avoidance [1][2][3][4]. The exploration and implementation of autonomous vehicles have the potential to alleviate road traffic congestion issues while reducing fuel consumption and carbon dioxide emissions, and thereby enhances the overall road efficiency.…”

Section: Introductionmentioning

confidence: 99%

Research on Behavioral Decision at an Unsignalized Roundabout for Automatic Driving Based on Proximal Policy Optimization Algorithm

Gan,

Zhang,

Liu

2024

Applied Sciences

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Unsignalized roundabouts have a significant impact on traffic flow and vehicle safety. To address the challenge of autonomous vehicles passing through roundabouts with low penetration, improve their efficiency, and ensure safety and stability, we propose the proximal policy optimization (PPO) algorithm to enhance decision-making behavior. We develop an optimization-based behavioral choice model for autonomous vehicles that incorporates gap acceptance theory and deep reinforcement learning using the PPO algorithm. Additionally, we employ the CoordConv network to establish an aerial view for spatial perception information gathering. Furthermore, a dynamic multi-objective reward mechanism is introduced to maximize the PPO algorithm’s reward pool function while quantifying environmental factors. Through simulation experiments, we demonstrate that our optimized PPO algorithm significantly improves training efficiency by enhancing the reward value function by 2.85%, 7.17%, and 19.58% in scenarios with 20, 100, and 200 social vehicles, respectively, compared to the PPO+CCMR algorithm. The effectiveness of simulation training also increases by 11.1%, 13.8%, and 7.4%. Moreover, there is a reduction in crossing time by 2.37%, 2.62%, and 13.96%. Our optimized PPO algorithm enhances path selection during autonomous vehicle simulation training as they tend to drive in the inner ring over time; however, the influence of social vehicles on path selection diminishes as their quantity increases. The safety of autonomous vehicles remains largely unaffected by our optimized PPO algorithm.

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Genetic Algorithm in Traffic Control and Autonomous Driving

Tousi,

Samizadeh,

Nikoofard

2024

Springer Tracts in Nature-Inspired Computing

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Decision Making for Autonomous Vehicles' Strategy in Triple-Lane Roundabout Intersections

Cited by 3 publications

References 0 publications

Multi‐criteria evolutionary optimization of a traffic light using genetics algorithm and teaching‐learning based optimization

Multi‐criteria evolutionary optimization of a traffic light using genetics algorithm and teaching‐learning based optimization

Research on Behavioral Decision at an Unsignalized Roundabout for Automatic Driving Based on Proximal Policy Optimization Algorithm

Genetic Algorithm in Traffic Control and Autonomous Driving

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