A novel intelligent vehicle risk assessment method combined with multi-sensor fusion in dense traffic environment

Purpose - The purpose of this paper is to accurately capture the risks which are caused by each road user in time. Design/methodology/approach - The authors proposed a novel risk assessment approach based on the multi-sensor fusion algorithm in the real traffic environment. Firstly, they proposed a novel detection-level fusion approach for multi-object perception in dense traffic environment based on evidence theory. This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception. The information of object type, position and velocity was accurately obtained. Then, they conducted several experiments in real dense traffic environment on highways and urban roads, which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios. By analyzing the generation process of traffic risks between vehicles and the road environment, the equivalent forces of vehicle–vehicle and vehicle–road were presented and theoretically calculated. The prediction steering angle and trajectory were considered in the determination of traffic risk influence area. Findings - The results of multi-object perception in the experiments showed that the proposed fusion approach achieved low false and missing tracking, and the road traffic risk was described as a field of equivalent force. The results extend the understanding of the traffic risk, which supported that the traffic risk from the front and back of the vehicle can be perceived in advance. Originality/value - This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception. The information of object type, position and velocity was used to reduce erroneous data association between tracks and detections. Then, the authors conducted several experiments in real dense traffic environment on highways and urban roads, which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios. By analyzing the generation process of traffic risks between vehicles and the road environment, the equivalent forces of vehicle–vehicle and vehicle–road were presented and theoretically calculated

Intelligent decision-making method for vehicles in emergency conditions based on artificial potential fields and finite state machines

This study aims to propose a decision-making method based on artificial potential fields (APFs) and finite state machines (FSMs) in emergency conditions. This study presents a decision-making method based on APFs and FSMs for emergency conditions. By modeling the longitudinal and lateral potential energy fields of the vehicle, the driving state is identified, and the trigger conditions are provided for path planning during lane changing. In addition, this study also designed the state transition rules based on the longitudinal and lateral virtual forces. It established the vehicle decision-making model based on the finite state machine to ensure driving safety in emergency situations. To illustrate the performance of the decision-making model by considering APFs and finite state machines. The version of the model in the co-simulation platform of MATLAB and CarSim shows that the developed decision model in this study accurately generates driving behaviors of the vehicle at different time intervals. The contributions of this study are two-fold. A hierarchical vehicle state machine decision model is proposed to enhance driving safety in emergency scenarios. Mathematical models for determining the transition thresholds of lateral and longitudinal vehicle states are established based on the vehicle potential field model, leading to the formulation of transition rules between different states of autonomous vehicles (AVs)

Active Control of Torsional Vibration during Mode Switching of Hybrid Powertrain Based on Adaptive Model Reference

When the energy management and coordinated control of the hybrid electric vehicle power system are not proper, torsional vibration problems will occur in various working states, especially in the mode switching process. These vibrations will affect the comfort, economy and emission of vehicles. In order to suppress the torsional vibration, this paper studied the active vibration control algorithm for the hybrid powertrains under the switching process of pure electric mode to hybrid mode. Primarily, the clutch combination process was divided into five stages and the dynamic models of the transmission system of each stage were established, respectively. Moreover, the principle of model reference adaptive control was analyzed. The applicability of the method to the torsional vibration of the driveline during mode switching was described. Furthermore, the clutch free displacement phase was used as the reference model. A model reference adaptive torsional vibration controller was built based on the controlled model. Finally, the efficacy of this active method for vibration reduction was simulated. The simulation results show that torsional vibration is most likely to occur in the speed coordination stage and the full participation stage. In these two stages, the designed controller can reduce the fluctuation of motor speed by 93.2% and 97.5%, respectively, the engine speed by 79.6% and 77.4%, respectively, the motor acceleration by 96.7% and 82.3%, respectively, and the engine acceleration by 88.9% and 82.3%, respectively. In addition, the controller can reduce the impact degree of the transmission system to within ±1 m/s3

A Novel Framework for Road Traffic Risk Assessment with HMM-Based Prediction Model

Over the past decades, there has been significant research effort dedicated to the development of intelligent vehicles and V2X systems. This paper proposes a road traffic risk assessment method for road traffic accident prevention of intelligent vehicles. This method is based on HMM (Hidden Markov Model) and is applied to the prediction of steering angle status to (1) evaluate the probabilities of the steering angle in each independent interval and (2) calculate the road traffic risk in different analysis regions. According to the model, the road traffic risk is quantified and presented directly in a visual form by the time-varying risk map, to ensure the accuracy of assessment and prediction. Experiment results are presented, and the results show the effectiveness of the assessment strategies

Toxoplasma gondii Rhoptry Protein 7 (ROP7) Interacts with NLRP3 and Promotes Inflammasome Hyperactivation in THP-1-Derived Macrophages

Toxoplasma gondii is a common opportunistic protozoan pathogen that can parasitize the karyocytes of humans and virtually all other warm-blooded animals. In the host’s innate immune response to T. gondii infection, inflammasomes can mediate the maturation of pro-IL-1β and pro-IL-18, which further enhances the immune response. However, how intercellular parasites specifically provoke inflammasome activation remains unclear. In this study, we found that the T. gondii secretory protein, rhoptry protein 7 (ROP7), could interact with the NACHT domain of NLRP3 through liquid chromatography-mass spectrometry analysis and co-immunoprecipitation assays. When expressing ROP7 in differentiated THP-1 cells, there was significant up-regulation in NF-κB and continuous release of IL-1β. This process is pyroptosis-independent and leads to inflammasome hyperactivation through the IL-1β/NF-κB/NLRP3 feedback loop. The loss of ROP7 in tachyzoites did not affect parasite proliferation in host cells but did attenuate parasite-induced inflammatory activity. In conclusion, these findings unveil that a T. gondii-derived protein is able to promote inflammasome activation, and further study of ROP7 will deepen our understanding of host innate immunity to parasites