Browsing by Author "Hang, Peng"
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Item Open Access Cooperative driving of connected autonomous vehicles in heterogeneous mixed traffic: a game theoretic approach(IEEE, 2024-05-13) Fang, Shiyu; Hang, Peng; Wei, Chongfeng; Xing, Yang; Sun, JianHigh-density, unsignalized intersections have always been a bottleneck of efficiency and safety. The emergence of Connected Autonomous Vehicles (CAVs) results in a mixed traffic condition, further increasing the complexity of the transportation system. Against this background, this paper aims to study the intricate and heterogeneous interaction of vehicles and conflict resolution at the high-density, mixed, unsignalized intersection. Theoretical insights about the interaction between CAVs and Human-driven Vehicles (HVs) and the cooperation of CAVs are synthesized, based on which a novel cooperative decision-making framework in heterogeneous mixed traffic is proposed. Normalized Cooperative game is concatenated with Level-k game (NCL game) to generate a system optimal solution. Then Lattice planner generates the optimal and collision-free trajectories for CAVs. To reproduce HVs in mixed traffic, interactions from naturalistic human driving data are extracted as prior knowledge. Non-cooperative game and Inverse Reinforcement Learning (IRL) are integrated to mimic the decision-making of heterogeneous HVs. Finally, three cases are conducted to verify the performance of the proposed algorithm, including the comparative analysis with different methods, the case study under different Rates of Penetration (ROP) and the interaction analysis with heterogeneous HVs. It is found that the proposed cooperative decision-making framework is beneficial to driving conflict resolution and the traffic efficiency improvement of the mixed unsignalized intersection. Besides, due to the consideration of driving heterogeneity, better human-machine interaction and cooperation can be realized in this paper.Item Open Access Driver steering behaviour modelling based on neuromuscular dynamics and multi‑task time‑series transformer(Springer, 2024-01-11) Xing, Yang; Hu, Zhongxu; Mo, Xiaoyu; Hang, Peng; Li, Shujing; Liu, Yahui; Zhao, Yifan; Lv, ChenDriver steering intention prediction provides an augmented solution to the design of an onboard collaboration mechanism between human driver and intelligent vehicle. In this study, a multi-task sequential learning framework is developed to predict future steering torques and steering postures based on upper limb neuromuscular electromyography signals. The joint representation learning for driving postures and steering intention provides an in-depth understanding and accurate modelling of driving steering behaviours. Regarding different testing scenarios, two driving modes, namely, both-hand and single-right-hand modes, are studied. For each driving mode, three different driving postures are further evaluated. Next, a multi-task time-series transformer network (MTS-Trans) is developed to predict the future steering torques and driving postures based on the multi-variate sequential input and the self-attention mechanism. To evaluate the multi-task learning performance and information-sharing characteristics within the network, four distinct two-branch network architectures are evaluated. Empirical validation is conducted through a driving simulator-based experiment, encompassing 21 participants. The proposed model achieves accurate prediction results on future steering torque prediction as well as driving posture recognition for both two-hand and single-hand driving modes. These findings hold significant promise for the advancement of driver steering assistance systems, fostering mutual comprehension and synergy between human drivers and intelligent vehicles.Item Open Access Guest editorial: Decision making and control for connected and automated vehicles(Institution of Engineering and Technology (IET), 2022-10-17) Lv, Chen; Hang, Peng; Xing, Yang; Nguyen, Anh-Tu; Jolfaei, AlirezaItem Open Access Learning from the Dark Side: a parallel time series modelling framework for forecasting and fault detection on intelligent vehicles(IEEE, 2023-12-13) Xing, Yang; Hu, Zhongxu; Hang, Peng; Lv, ChenTime series vehicle state modelling is crucial in various real-world applications, such as fault detection, fault tolerance, optimization, and cyber security for intelligent vehicles (IVs). In this study, we propose a novel parallel time series modeling framework (PTSM) to forecast and detect vehicle braking cylinder pressure states, thereby enhancing the safety of the braking system. Specifically, the PTSM consists of two branches: LightNet and DarkNet. The LightNet learns time-series (TS) representations of real-world signals to forecasts and identifies vehicle states. On the other hand, the DarkNet employs a novel multi-task learning and dual Relativistic Generative Adversarial Network (dual-RaGAN) framework to reconstructs healthy sequential states, detects faults, and forecasts future vehicle states using synthesized faulty sequences. To develop the PTSM framework, we introduce a novel data processing and random fault synthesizing method. We evaluate the performance of the dual-RaGAN model using real-world data and compare it with non-adversarial approaches, demonstrating the efficiency of the multi-task generative sequential representation. Extensive experimental results show that by integrating knowledge from the dark side, real-world time-series modelling (TSM) for forecasting and fault detection can be significantly improved, with a 34.7% enhancement in forecasting and an 11% improvement in fault recognition. The results indicate that signal reconstruction leads to more accurate sequence forecasting and fault recognition in both the dark and light sides. This proposed study not only introduces a novel time-series modelling framework but also establishes a new approach for vehicle testing, fault detection, and cyber security research for intelligent vehicles. Data and Codes are available at: https://github.com/YXING-CC/Dark-Light.