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Sun Bohua,Deng Weiwen,Wu Jian,Li Yaxin,Wang Jinsong 한국자동차공학회 2020 International journal of automotive technology Vol.21 No.6
Autonomous vehicles are aiming at improving driving safety and comfort. They need to perform socially accepted behaviors in complex urban scenarios including human-driven vehicles with uncertain intentions. What’s more, understanding human drivers’ driving styles that make the systems more human-like or personalized is the key to improve the system performance, in particular, the acceptance and adaption of autonomous vehicles to human passengers. In this study, a personalized intention-aware autonomous driving strategy is proposed. An online driving style identification is proposed based on double-level Multi-dimension Gaussian Hidden Markov Process (MGHMP) with arbitration mechanism and evaluated in field test. A Mixed Observable Markov Decision Process (MOMDP) is built to model the general personalized intention-aware framework. A human-like policy generation mechanism is used to generate the possible candidates to overcome the difficulty in solving MOMDP. The index of surrounding vehicles’ intention of the upper-level MGHMP is updated during each prediction time step. The weighting factors of the reward function are configured with the identification result of lower-level MGHMP. The personalized intention-aware autonomous driving strategy is evaluated on a Real-Time Intelligent Simulation Platform. Results show that the proposed strategy can achieve the online identification accuracy above 95 % and for personalized autonomous driving in scenarios mixed with human-driven vehicles with uncertain intentions.
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RESEARCH ON MILLIMETER WAVE RADAR SIMULATION MODEL FOR INTELLIGENT VEHICLE
Xin Li,Weiwen Deng,Sumin Zhang,Yaxin Li,Shiping Song,Shanshan Wang,Guanyu Wang 한국자동차공학회 2020 International journal of automotive technology Vol.21 No.2
Radar simulation models can effectively overcome the drawbacks of real vehicle experiment and speed up the development process of intelligent vehicle technologies based on millimeter wave radar via virtual testing. However, there are still many gaps between the radar model using in the virtual driving environment and the real radar. In this paper, a novel simulation model of intelligent vehicle millimeter wave radar is proposed. Based on the analysis of the real radar performance in typical application scenes, the radar model considers the mechanism and characteristics of the vehicle radar synthetically and a systematic radar modeling architecture with innovation is introduced. The highlights of this radar model include the design of the RCS simulation model for radar targets with both high accuracy and real-time performance, the establishment of the quantitative false alarm model, missed detection model and measurement error simulation model. Vast amounts of data collected by real vehicle radar are applied to fetch model parameters and verify the accuracy of the radar model. Simulation results show that the proposed model can reach both high reliability and computational efficiency.
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Yang Zhao,Weiwen Deng,Jian Wu,Rui He 한국자동차공학회 2017 International journal of automotive technology Vol.18 No.4
This paper proposes a constrained optimization-based torque control allocation method aimed to improve energy efficiency, and thus, driving range for electric vehicles. In the proposed method, the cost function is defined not only to achieve desired yaw moment for vehicle handling and stability, but also to minimize power losses for energy efficiency. The particular attention is paid to the power losses due to tire slips both longitudinally and laterally. The constraints are also set based on thorough investigation on various causes of power disppation such that the torque is allocated with restraint to use regenerative braking in its maximum capacity. The proposed control allocation method has been tested and verified to be effective on energy efficiency improvement through both simulation and experiment under various driving maneuvers.
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Human dynamics based driver model for autonomous car
Li, Lin,Liu, Yanheng,Wang, Jian,Deng, Weiwen,Oh, Heekuck IET 2016 IET intelligent transport systems Vol.10 No.8
<P>This study presents a new driver model based on human behaviour dynamics for autonomous cars, which allows driverless cars to move appropriately in accordance to the behavioural features of driver owners. This model is established through analysing drivers' various properties, e.g. gender, age, driving experience, personality, and emotion. These attributes collectively determine all the actions occurred during the driving process. Through analysing the statistical data gathered during the simulation, the authors find that the proposed model can reflect the power-law distribution with respect to the concerned human behaviours. Finally, the proposed model is validated by the hardware-in-loop simulator and real driving experiment.</P>
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