Design of Autonomous and Manual Driving System for 4WIS4WID Vehicle

Li, Tzuu-Hseng S.; Lee, Ming-Han; Lin, Ching-Yi; Liou, Guan-Hong; Chen, Wei‐Chung

doi:10.1109/access.2016.2548081

Cited by 18 publications

(11 citation statements)

References 36 publications

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“…Again, autonomous driving has attracted remarkable attention in recent years in diverse traffic scenarios, including lane changing [12,13], reverse parking [14], parallel parking [15], longitudinal control [16], computing resource allocation [17] and motion/trajectory design [18][19][20][21][22]. Among all these challenges, the planning of the AVs' trajectory is fundamental.…”

Section: A State-of-the-artmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing the Fuel-Economy of V2I-Assisted Autonomous Driving: A Reinforcement Learning Approach

Liu

Yue

et al. 2020

IEEE Trans. Veh. Technol.

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A novel framework is proposed for enhancing the driving safety and fuel economy of autonomous vehicles (AVs) with the aid of vehicle-to-infrastructure (V2I) communication networks. The problem of driving trajectory design is formulated for minimizing the total fuel consumption, while enhancing driving safety (by obeying the traffic rules and avoiding obstacles). In an effort to solve this pertinent problem, a deep reinforcement learning (DRL) approach is proposed for making collision-free decisions. Firstly, a deep Q-network (DQN) aided algorithm is proposed for determining the trajectory and velocity of the AV by receiving real-time traffic information from the base stations (BSs). More particularly, the AV acts as an agent to carry out optimal action such as lane change and velocity change by interacting with the environment. Secondly, to overcome the large overestimation of action values by the Q-learning model, a double deep Q-network (DDQN) algorithm is proposed by decomposing the max-Q-value operation into action selection and action evaluation. Additionally, three practical driving policies are also proposed as benchmarks. Numerical results are provided for demonstrating that the proposed trajectory design algorithms are capable of enhancing the driving safety and fuel economy of AVs. We demonstrate that the proposed DDQN based algorithm outperforms the DQN based algorithm. Additionally, it is also demonstrated that the proposed fuel-economy (FE) based driving policy derived from the DRL algorithm is capable of achieving in excess of 24% of fuel savings over the benchmarks.

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Section: A State-of-the-artmentioning

confidence: 99%

“…As for the reward function, when the AV carries out an action for fuel-saving and safe driving, a constant reward value is achieved. A typical reward function can be formulated as (15) at the top of the this page.…”

Section: B Proposed Dqn Based Trajectory Design Algorithmmentioning

confidence: 99%

Enhancing the Fuel-Economy of V2I-Assisted Autonomous Driving: A Reinforcement Learning Approach

Liu

Yue

et al. 2020

IEEE Trans. Veh. Technol.

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“…In 2016, a 4WIS4WID 10,11 vehicle was proposed, in which the judgments of vision with the fuzzy control methods were integrated to ensure the correct motion of the vehicle. The vehicle was able to change its velocity in a timely manner under any condition and was able to move in a curved and narrow lane successfully.…”

Section: Related Workmentioning

confidence: 99%

A deep learning platooning-based video information-sharing Internet of Things framework for autonomous driving systems

Zhou

Akhtar

Man

et al. 2019

International Journal of Distributed Sensor Networks

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To enhance the safety and stability of autonomous vehicles, we present a deep learning platooning-based video information-sharing Internet of Things framework in this study. The proposed Internet of Things framework incorporates concepts and mechanisms from several domains of computer science, such as computer vision, artificial intelligence, sensor technology, and communication technology. The information captured by camera, such as road edges, traffic lights, and zebra lines, is highlighted using computer vision. The semantics of highlighted information is recognized by artificial intelligence. Sensors provide information on the direction and distance of obstacles, as well as their speed and moving direction. The communication technology is applied to share the information among the vehicles. Since vehicles have high probability to encounter accidents in congested locations, the proposed system enables vehicles to perform self-positioning with other vehicles in a certain range to reinforce their safety and stability. The empirical evaluation shows the viability and efficacy of the proposed system in such situations. Moreover, the collision time is decreased considerably compared with that when using traditional systems.

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“…self-driving cars, robots, etc. [1] and that have made the initial pathway towards the development of advanced decision-making facilities. Furthermore, such decision-making facilities have also been supported by internal and external IoT (i.e.…”

Section: Introduction Autonomy In the Oceanmentioning

confidence: 99%

Deep Learning Toward Autonomous Ship Navigation and Possible COLREGs Failures

Perera

2019

Journal of Offshore Mechanics and Arctic Engineering

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A structured technology framework to address navigation considerations, including collision avoidance, of autonomous ships is the focus of this study. That consists of adequate maritime technologies to achieve the required level of navigation integrity in ocean autonomy. Since decision-making facilities in future autonomous vessels can play an important role under ocean autonomy, these technologies should consist of adequate system intelligence. Such system intelligence should consider localized decision-making modules to facilitate a distributed intelligence type strategy that supports distinct navigation situations in future vessels as agent-based systems. The main core of this agent consists of deep learning type technology that has presented promising results in other transportation systems, i.e., self-driving cars. Deep learning can capture helmsman behavior; therefore, such system intelligence can be used to navigate future autonomous vessels. Furthermore, an additional decision support layer should also be developed to facilitate deep learning-type technologies, where adequate solutions to distinct navigation situations can be facilitated. Collision avoidance under situation awareness, as one of such distinct navigation situations (i.e., a module of the decision support layer), is extensively discussed. Ship collision avoidance is regulated by the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) under open sea areas. Hence, a general overview of the COLREGs and its implementation challenges, i.e., possible regulatory failures, under situation awareness of autonomous ships is also presented with the possible solutions. Additional considerations, i.e., performance standards with the applicable limits of liability, terms, expectations, and conditions, toward evaluating ship behavior as an agent-based system in collision avoidance situations are also illustrated.

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Design of Autonomous and Manual Driving System for 4WIS4WID Vehicle

Cited by 18 publications

References 36 publications

Enhancing the Fuel-Economy of V2I-Assisted Autonomous Driving: A Reinforcement Learning Approach

Enhancing the Fuel-Economy of V2I-Assisted Autonomous Driving: A Reinforcement Learning Approach

A deep learning platooning-based video information-sharing Internet of Things framework for autonomous driving systems

Deep Learning Toward Autonomous Ship Navigation and Possible COLREGs Failures

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