Comparison of PPO and SAC Algorithms Towards Decision Making Strategies for Collision Avoidance Among Multiple Autonomous Vehicles

Muzahid, Abu Jafar Md; Kamarulzaman, Syafiq Fauzi; Rahman, Arafatur

doi:10.1109/icsecs52883.2021.00043

Cited by 19 publications

(8 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Liao, et al [6] utilized the dueling deep Q-network (DDQN) to obtain a highway decision-making strategy. Muzahid, et al [30] proposed a centralized multi-vehicle control strategy by reinforcement learning (RL) and compared soft actor-critic (SAC) and proximal policy optimization (PPO) algorithms. Duan, et al [30] proposed a hierarchical structure for learning driving strategies using the RL method.…”

Section: A Decision-making Methodsmentioning

confidence: 99%

“…Muzahid, et al [30] proposed a centralized multi-vehicle control strategy by reinforcement learning (RL) and compared soft actor-critic (SAC) and proximal policy optimization (PPO) algorithms. Duan, et al [30] proposed a hierarchical structure for learning driving strategies using the RL method. Chen, et al [25] built a hierarchical deep deterministic policy gradient (DDPG) algorithm and proposed an attention mechanism for learning driving strategies using images.…”

Section: A Decision-making Methodsmentioning

confidence: 99%

See 1 more Smart Citation

The Shanghai alliance of multilingual researchers: Fudan University, Tongji University, Shanghai University of Finance and Economics, and Shanghai International Studies University, China

et al. 2022

View full text Add to dashboard Cite

Patient-independent detection of epileptic activities based on visual spectral representation of continuous EEG (cEEG) has been widely used for diagnosing epilepsy. However, precise detection remains a considerable challenge due to subtle variabilities across subjects, channels and time points. Thus, capturing fine-grained, discriminative features of EEG patterns, which is associated with high-frequency textural information, is yet to be resolved. In this work, we propose Scattering Transformer (ScatterFormer), an invariant scattering transform-based hierarchical Transformer that specifically pays attention to subtle features. In particular, the disentangled frequency-aware attention (FAA) enables the Transformer to capture clinically informative high-frequency components, offering a novel clinical explainability based on visual encoding of multichannel EEG signals. Evaluations on two distinct tasks of epileptiform detection demonstrate the effectiveness our method. Our proposed model achieves median AUCROC and accuracy of 98.14%, 96.39% in patients with Rolandic epilepsy. On a neonatal seizure detection benchmark, it outperforms the state-of-the-art by 9% in terms of average AUCROC.

show abstract

Section: A Decision-making Methodsmentioning

confidence: 99%

Section: A Decision-making Methodsmentioning

confidence: 99%

The Shanghai alliance of multilingual researchers: Fudan University, Tongji University, Shanghai University of Finance and Economics, and Shanghai International Studies University, China

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Finally, when the ego vehicle detects more than one participant in the range of the standard distance, it will make an extra careful driving situation. We need to train our model using a trial and error process to adopt our kinematic constraints 168 . Figure 7 shows the proposed conceptual framework for MVCCA in AVs, and in the following sections, we will discuss briefly all five phases.…”

Section: Conceptual Framework Of Mvccamentioning

confidence: 99%

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Muzahid

Kamarulzaman

Rahman

et al. 2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

Prospective customers are becoming more concerned about safety and comfort as the automobile industry swings toward automated vehicles (AVs). A comprehensive evaluation of recent AVs collision data indicates that modern automated driving systems are prone to rear-end collisions, usually leading to multiple-vehicle collisions. Moreover, most investigations into severe traffic conditions are confined to single-vehicle collisions. This work reviewed diverse techniques of existing literature to provide planning procedures for multiple vehicle cooperation and collision avoidance (MVCCA) strategies in AVs while also considering their performance and social impact viewpoints. Firstly, we investigate and tabulate the existing MVCCA techniques associated with single-vehicle collision avoidance perspectives. Then, current achievements are extensively evaluated, challenges and flows are identified, and remedies are intelligently formed to exploit a taxonomy. This paper also aims to give readers an AI-enabled conceptual framework and a decision-making model with a concrete structure of the training network settings to bridge the gaps between current investigations. These findings are intended to shed insight into the benefits of the greater efficiency of AVs set-up for academics and policymakers. Lastly, the open research issues discussed in this survey will pave the way for the actual implementation of driverless automated traffic systems.

show abstract

“…In order to address the subsequent chain events of an autonomous vehicle chain collision as well as the traffic situation ontology, we will examine the problem of collision avoidance as a Markov Decision Process that can be addressed using DRL in this section. Our earlier study [43] compared two DRL methods as the foundation for selecting the DRL methodology in this work for more in-depth examination and investigation. In comparison to previous approaches such as mathematics and physics-based methods, chain collision avoidance applications using DRL do not necessitate the use of a significant mathematical model.…”

Section: ) Chain Collision Avoidance Techniquesmentioning

confidence: 99%

Deep Reinforcement Learning-Based Driving Strategy for Avoidance of Chain Collisions and Its Safety Efficiency Analysis in Autonomous Vehicles

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

Comparison of PPO and SAC Algorithms Towards Decision Making Strategies for Collision Avoidance Among Multiple Autonomous Vehicles

Cited by 19 publications

References 19 publications

The Shanghai alliance of multilingual researchers: Fudan University, Tongji University, Shanghai University of Finance and Economics, and Shanghai International Studies University, China

The Shanghai alliance of multilingual researchers: Fudan University, Tongji University, Shanghai University of Finance and Economics, and Shanghai International Studies University, China

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Deep Reinforcement Learning-Based Driving Strategy for Avoidance of Chain Collisions and Its Safety Efficiency Analysis in Autonomous Vehicles

Contact Info

Product

Resources

About