6 research outputs found
CPSOR-GCN: A Vehicle Trajectory Prediction Method Powered by Emotion and Cognitive Theory
Active safety systems on vehicles often face problems with false alarms. Most
active safety systems predict the driver's trajectory with the assumption that
the driver is always in a normal emotion, and then infer risks. However, the
driver's trajectory uncertainty increases under abnormal emotions. This paper
proposes a new trajectory prediction model: CPSOR-GCN, which predicts vehicle
trajectories under abnormal emotions. At the physical level, the interaction
features between vehicles are extracted by the physical GCN module. At the
cognitive level, SOR cognitive theory is used as prior knowledge to build a
Dynamic Bayesian Network (DBN) structure. The conditional probability and state
transition probability of nodes from the calibrated SOR-DBN quantify the causal
relationship between cognitive factors, which is embedded into the cognitive
GCN module to extract the characteristics of the influence mechanism of
emotions on driving behavior. The CARLA-SUMO joint driving simulation platform
was built to develop dangerous pre-crash scenarios. Methods of recreating
traffic scenes were used to naturally induce abnormal emotions. The experiment
collected data from 26 participants to verify the proposed model. Compared with
the model that only considers physical motion features, the prediction accuracy
of the proposed model is increased by 68.70%. Furthermore,considering the
SOR-DBN reduces the prediction error of the trajectory by 15.93%. Compared with
other advanced trajectory prediction models, the results of CPSOR-GCN also have
lower errors. This model can be integrated into active safety systems to better
adapt to the driver's emotions, which could effectively reduce false alarms.Comment: 15 pages, 31 figures, submitted to IEEE Transactions on Intelligent
Vehicle
Human-like Decision Making and Motion Control for Smooth and Natural Car Following
Car-following is an important driving behaviour for intelligent vehicles and has a significant impact on traffic efficiency and traffic safety. Car-following models are widely developed to characterize the human-drivers car-following manoeuvre actions and adopted in traffic simulation and automated vehicle control system development. Car-following models need to be able to represent the drivers behaviour while following preceding vehicles. On the other hand, car-following controllers are an important component of intelligent vehicle systems, both for autonomous vehicles and connected vehicles. However, Adaptive Cruise Control (ACC) as well as Cooperative Adaptive Cruise Control (CACC) do not include human behaviour, which makes their car-following behaviour not human-like or natural for the on-board driver or passenger. To address this problem, in this study, the human-like Wiedemann car-following model is calibrated and verified with our driving simulator data. A human-like car-following nonlinear model predictive control (MPC) controller is developed based on the calibrated car-following model. Three different scenarios are tested to evaluate the performance of the proposed controller, with which the autonomous vehicle is able to have human-like and smooth trajectories at different phases and within different transition zones
Lane-Change Intention Estimation for Car-Following Control in Autonomous Driving
Car-following is the most general behavior in highway driving. It is crucial to recognize the cut-in intention of vehicles from an adjacent lane for safe and cooperative driving. In this paper, a method of behavior estimation is proposed to recognize and predict the lane change intentions based on the contextual traffic information. A model predictive controller is designed to optimize the acceleration sequences by incorporating the lane-change intentions of other vehicles. The public data set of next generation simulation is labeled and then published as a benchmarking platform for the research community. Experimental results demonstrate that the proposed method can accurately estimate vehicle behavior and therefore outperform the traditional car-following control.Accepted author manuscriptCyber Securit