Autonomous Driving Control Based on the Technique of Semantic Segmentation

Tsai, Jichiang; Chang, Che-Cheng; Li, Tzu

doi:10.3390/s23020895

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…To train autonomous driving behaviors effectively in dynamic traffic environments [35,36], we employ a semantic segmentation camera on the training vehicle. This camera captures the driving scene and uses semantic segmentation to classify each pixel, simplifying the visual data by focusing on relevant features and reducing complexity.…”

Section: Driving Scenesmentioning

confidence: 99%

“…First of all, the concept of navigation with waypoints used in [35] is adopted to prevent the training car from deviating from the driving path properly arranged when it crosses the intersection. Before each behavior, a sequence of waypoints will be generated via navigation in advance, as seen in the example shown in Figure 8 for the going-straight behavior of crossing the intersection.…”

Section: The Reward Mechanismmentioning

confidence: 99%

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Autonomous Driving Control for Passing Unsignalized Intersections Using the Semantic Segmentation Technique

Tsai,

Chang,

Chen

et al. 2024

Electronics

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Autonomous driving in urban areas is challenging because it requires understanding vehicle movements, traffic rules, map topologies and unknown environments in the highly complex driving environment, and thus typical urban traffic scenarios include various potentially hazardous situations. Therefore, training self-driving cars by using traditional deep learning models not only requires the labelling of numerous datasets but also takes a large amount of time. Because of this, it is important to find better alternatives for effectively training self-driving cars to handle vehicle behavior and complex road shapes in dynamic environments and to follow line guidance information. In this paper, we propose a method for training a self-driving car in simulated urban traffic scenarios to be able to judge the road conditions on its own for crossing an unsignalized intersection. In order to identify the behavior of traffic flow at the intersection, we use the CARLA (CAR Learning to Act) self-driving car simulator to build the intersection environment and simulate the process of traffic operation. Moreover, we attempt to use the DDPG (Deep Deterministic Policy Gradient) and RDPG (Recurrent Deterministic Policy Gradient) learning algorithms of the DRL (Deep Reinforcement Learning) technology to train models based on the CNN (Convolutional Neural Network) architecture. Specifically, the observation image of the semantic segmentation camera installed on the self-driving car and the vehicle speed are used as the model input. Moreover, we design an appropriate reward mechanism for performing training according to the current situation of the self-driving car judged from sensing data of the obstacle sensor, collision sensor and lane invasion detector. Doing so can improve the convergence speed of the model to achieve the purpose of the self-driving car autonomously judging the driving paths so as to accomplish accurate and stable autonomous driving control.

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Section: Driving Scenesmentioning

confidence: 99%

Section: The Reward Mechanismmentioning

confidence: 99%

Autonomous Driving Control for Passing Unsignalized Intersections Using the Semantic Segmentation Technique

Tsai,

Chang,

Chen

et al. 2024

Electronics

Self Cite

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“…In [23], the authors use the Deep Deterministic Policy Gradient (DDPG) algorithm to adjust the virtual inertia and damping coefficient of the islanded VSG. DDPG, being a reinforcement learning algorithm based on the actor-critic framework, operates in continuous state and action spaces [24][25][26]. Nevertheless, it is well-documented that DDPG is susceptible to overestimation bias, potentially resulting in suboptimal control policies.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Control for Virtual Synchronous Generator Parameters Based on Soft Actor Critic

Lu,

Zhuan

2024

Sensors

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This paper introduces a model-free optimization method based on reinforcement learning (RL) aimed at resolving the issues of active power and frequency oscillations present in a traditional virtual synchronous generator (VSG). The RL agent utilizes the active power and frequency response of the VSG as state information inputs and generates actions to adjust the virtual inertia and damping coefficients for an optimal response. Distinctively, this study incorporates a setting-time term into the reward function design, alongside power and frequency deviations, to avoid prolonged system transients due to over-optimization. The soft actor critic (SAC) algorithm is utilized to determine the optimal strategy. SAC, being model-free with fast convergence, avoids policy overestimation bias, thus achieving superior convergence results. Finally, the proposed method is validated through MATLAB/Simulink simulation. Compared to other approaches, this method more effectively suppresses oscillations in active power and frequency and significantly reduces the setting time.

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“…Since the last few decades, advanced driver assistance systems (ADAS) are equally appreciated to avoid traffic accidents and to improve driving comfort in autonomous vehicles [11]. The ADAS systems are safe and secure systems designed to decrease the human error rate.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent System to Sense Textual Cues for Location Assistance in Autonomous Vehicles

Unar

Liu

et al. 2023

Sensors

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The current technological world is growing rapidly and each aspect of life is being transformed toward automation for human comfort and reliability. With autonomous vehicle technology, the communication gap between the driver and the traditional vehicle is being reduced through multiple technologies and methods. In this regard, state-of-the-art methods have proposed several approaches for advanced driver assistance systems (ADAS) to meet the requirement of a level-5 autonomous vehicle. Consequently, this work explores the role of textual cues present in the outer environment for finding the desired locations and assisting the driver where to stop. Firstly, the driver inputs the keywords of the desired location to assist the proposed system. Secondly, the system will start sensing the textual cues present in the outer environment through natural language processing techniques. Thirdly, the system keeps matching the similar keywords input by the driver and the outer environment using similarity learning. Whenever the system finds a location having any similar keyword in the outer environment, the system informs the driver, slows down, and applies the brake to stop. The experimental results on four benchmark datasets show the efficiency and accuracy of the proposed system for finding the desired locations by sensing textual cues in autonomous vehicles.

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Autonomous Driving Control Based on the Technique of Semantic Segmentation

Cited by 5 publications

References 18 publications

Autonomous Driving Control for Passing Unsignalized Intersections Using the Semantic Segmentation Technique

Autonomous Driving Control for Passing Unsignalized Intersections Using the Semantic Segmentation Technique

Adaptive Control for Virtual Synchronous Generator Parameters Based on Soft Actor Critic

An Intelligent System to Sense Textual Cues for Location Assistance in Autonomous Vehicles

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