Machine reading comprehension (MRC) is a challenging NLP task for it requires to carefully deal with all linguistic granularities from word, sentence to passage.
With the success of vision-language pre-training, we have witnessed the state-of-the-art has been pushed on multi-modal understanding and generation.
This paper considers a single-population model with age structure and psychological effects in a polluted environment.
Specifically, we first extract multiple code views using compiler tools, and learn the complementary information among them under a contrastive learning framework.
We present results on the experimental validation of leading cruise control (LCC) for connected and autonomous vehicles (CAVs).
For the control of connected and autonomous vehicles (CAVs), most existing methods focus on model-based strategies.
Formation control methods of connected and automated vehicles have been proposed to smoothly switch the structure of vehicular formations in different scenarios.
However, the operation of a bus fleet is unstable in nature, and bus bunching has become a common phenomenon that undermines the efficiency and reliability of bus systems.
In this paper, instead of relying on a parametric car-following model, we introduce a data-driven predictive control strategy to achieve safe and optimal control for CAVs in mixed traffic.
In recent years, a great progress has been witnessed for cross-domain object detection.
Specifically, given a math word problem, the model first retrieves similar questions by a memory module and then encodes the unsolved problem and each retrieved question using a representation module.
Unsignalized intersection cooperation of connected and automated vehicles (CAVs) is able to eliminate green time loss of signalized intersections and improve traffic efficiency.
However, due to the significant uncertainties in passenger demand and traffic conditions, bus operation is unstable in nature and bus bunching has become a common phenomenon that undermines the reliability and efficiency of bus services.
Multi-vehicle coordinated decision making and control can improve traffic efficiency while guaranteeing driving safety.
Most existing strategies for CAVs' longitudinal control focus on downstream traffic conditions, but neglect the impact of CAVs' behaviors on upstream traffic flow.
Unfortunately, existing methods either focus on verifying a single network or rely on loose approximations to prove the equivalence of two networks.
In mixed traffic flow consisting of AVs and human-driven vehicles (HDVs), the prevailing platooning of multiple AVs is not the only choice for cooperative formation.
Numerical studies confirm the potential of LCC to strengthen the capability of CAVs in suppressing traffic instabilities and smoothing traffic flow.
Systems and Control Systems and Control Optimization and Control
Instead of learning a reliable behavior for ego automated vehicle, we focus on how to improve the outcomes of the total transportation system by allowing each automated vehicle to learn cooperation with each other and regulate human-driven traffic flow.
Data collection and annotation are time-consuming in machine learning, expecially for large scale problem.
Sophisticated automatic incident detection (AID) technology plays a key role in contemporary transportation systems.
We argue that if the bias-variance trade-off is to be better balanced by a more effective feature selection method unlabeled data is very likely to boost the classification performance.