Human–Machine Interaction in Driving Assistant Systems for Semi-Autonomous Driving Vehicles

Lee, Heung-Gu; Kang, Dong Heon; Kim, Deok‐Hwan

doi:10.3390/electronics10192405

Cited by 4 publications

(2 citation statements)

References 52 publications

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“…The mobile edge computing environment introduced in Section 4 is used for experiments. CARLA, a virtual urban driving simulator, and ROS are used to build an autonomous driving environment and to transmit data between virtual vehicles and cities [32]. The hardware and software specifications are shown in Table 6.…”

Section: Methodsmentioning

confidence: 99%

Task Offloading of Deep Learning Services for Autonomous Driving in Mobile Edge Computing

2023

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As the utilization of complex and heavy applications increases in autonomous driving, research on using mobile edge computing and task offloading for autonomous driving is being actively conducted. Recently, researchers have been studying task offloading algorithms using artificial intelligence, such as reinforcement learning or partial offloading. However, these methods require a lot of training data and critical deadlines and are weakly adaptive to complex and dynamically changing environments. To overcome this weakness, in this paper, we propose a novel task offloading algorithm based on Lyapunov optimization to maintain the system stability and minimize task processing delay. First, a real-time monitoring system is built to utilize distributed computing resources in an autonomous driving environment efficiently. Second, the computational complexity and memory access rate are analyzed to reflect the characteristics of the deep learning applications to the task offloading algorithm. Third, Lyapunov and Lagrange optimization solves the trade-off issues between system stability and user requirements. The experimental results show that the system queue backlog remains stable, and the tasks are completed within an average of 0.4231 s, 0.7095 s, and 0.9017 s for object detection, driver profiling, and image recognition, respectively. Therefore, we ensure that the proposed task offloading algorithm enables the deep learning application to be processed within the deadline and keeps the system stable.

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Section: Methodsmentioning

confidence: 99%

Task Offloading of Deep Learning Services for Autonomous Driving in Mobile Edge Computing

2023

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“…This communication has to be both distraction-free, to ensure safety, and efficient to ensure that the driving cues or alarms generated by ADAS are displayed timely and clearly to the human driver. Different concepts of the HMI for ADAS have been proposed in the literature, mostly from the viewpoint of ergonomics [41][42][43]; however, there is a lack of literature dealing explicitly with HMIs for city buses. Whereas the information can be provided to the driver through visual, acoustic and haptic modalities, including force feedback on the steering wheel, our ADAS uses a relatively simple single-display graphical HMI with additional audible warnings.…”

Section: Related Workmentioning

confidence: 99%

GNSS-Based Driver Assistance for Charging Electric City Buses: Implementation and Lessons Learned from Field Testing

Esfandiyar,

Ćwian,

Nowicki

et al. 2023

Remote Sensing

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Modern public transportation in urban areas increasingly relies on high-capacity buses. At the same time, the share of electric vehicles is increasing to meet environmental standards. This introduces problems when charging these vehicles from chargers at bus stops, as untrained drivers often find it difficult to execute docking manoeuvres on the charger. A practical solution to this problem requires a suitable advanced driver-assistance system (ADAS), which is a system used to automatise and make safer some of the tasks involved in driving a vehicle. In the considered case, ADAS supports docking to the electric charging station, and thus, it must solve two issues: precise positioning of the bus relative to the charger and motion planning in a constrained space. This paper addresses these issues by employing GNSS-based positioning and optimisation-based planning, resulting in an affordable solution to the ADAS for the docking of electric buses while recharging. We focus on the practical side of the system, showing how the necessary features were attained at a limited hardware and installation cost, also demonstrating an extensive evaluation of the fielded ADAS for an operator of public transportation in the city of Poznań in Poland.

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GR‐Former: Graph‐reinforcement transformer for skeleton‐based driver action recognition

Xu,

2024

IET Computer Vision

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In in‐vehicle driving scenarios, composite action recognition is crucial for improving safety and understanding the driver's intention. Due to spatial constraints and occlusion factors, the driver's range of motion is limited, thus resulting in similar action patterns that are difficult to differentiate. Additionally, collecting skeleton data that characterise the full human posture is difficult, posing additional challenges for action recognition. To address the problems, a novel Graph‐Reinforcement Transformer (GR‐Former) model is proposed. Using limited skeleton data as inputs, by introducing graph structure information to directionally reinforce the effect of the self‐attention mechanism, dynamically learning and aggregating features between joints at multiple levels, the authors’ model constructs a richer feature vector space, enhancing its expressiveness and recognition accuracy. Based on the Drive & Act dataset for composite action recognition, the authors’ work only applies human upper‐body skeleton data to achieve state‐of‐the‐art performance compared to existing methods. Using complete human skeleton data also has excellent recognition accuracy on the NTU RGB + D‐ and NTU RGB + D 120 dataset, demonstrating the great generalisability of the GR‐Former. Generally, the authors’ work provides a new and effective solution for driver action recognition in in‐vehicle scenarios.

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Human–Machine Interaction in Driving Assistant Systems for Semi-Autonomous Driving Vehicles

Cited by 4 publications

References 52 publications

Task Offloading of Deep Learning Services for Autonomous Driving in Mobile Edge Computing

Task Offloading of Deep Learning Services for Autonomous Driving in Mobile Edge Computing

GNSS-Based Driver Assistance for Charging Electric City Buses: Implementation and Lessons Learned from Field Testing

GR‐Former: Graph‐reinforcement transformer for skeleton‐based driver action recognition

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