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The main objective of platoon control is coordinated motion of autonomous vehicle platooning with small intervehicle spacing while maintaining the same speed and acceleration as the leading vehicle, which can save energy consumption and improve traffic throughput. The conventional platoon control methods are confronted with the problem of manual parameter tuning. In order to addres this isue, a novel bifold platoon control approach leveraging a deep reinforcement learning-based model is proposed, which enables the platoon adapt to the complex traffic environment, and guarantees the safety of platoon. The upper layer controller based on the TD3 tuned PID algorithm outputs the desired acceleration. This integration mitigates the inconvenience of frequent manual parameter tuning asociated with the conventional PID algorithm. The lower layer controller tracks the desired acceleration based on the inverse vehicle dynamics model and feedback control.

The development of intelligent transportation improves road efficiency, reduces automobile energy consumption, and improves driving safety. The core of intelligent transportation is the two-way information interaction between vehicles and the road environment. At present, road environmental information can flow to the vehicle, while the vehicle’s information rarely flows to the outside world. The electronic throttle and electronic braking systems of some vehicles use sensors to get the state of the accelerator and brake pedal, which can be transmitted to the outside environment through technologies such as the Internet of Vehicles. But the Internet of Vehicles technology has not been widely used, and it relies on signal sources, which is a passive way of information acquisition. In this paper, an active identification method is proposed to get the vehicle pedal on-off state as well as the driver’s operation behavior through existing traffic facilities.

The Alpha Magnetic Spectrometer (AMS) is a particle physics detector designed to measure charged cosmic rays spectra and high energy photons on board of the International Space Station (ISS). The large acceptance (0.5 m2sr), the long mission duration (3 years) and the state of the art particle identification techniques will allow AMS to provide the most sensitive search up to date for the existence of anti matter nuclei and for the origin of dark matter. AMS02 now is in its final integration phase at CERN. To verify the functional performance of the detectors and of the key subsystems of the Thermal Control System under vacuum condition and to validate the thermal mathematical model of AMS02 a system level thermo-vacuum test will be performed in the Large Space Simulator (LSS) of ESA at ESTEC (the Netherlands).

This paper reports on the approach followed for the TCS verification of the payload AMS-02 (Alpha Magnetic Spectrometer), aiming at the qualification of the entire system, in steps, for the space environment. AMS-02 is a state-of-the-art experiment composed by a stack of seven different particle detectors, each of them having its own electronics and control equipments. It will be installed on the International Space Station Starboard segment S3 of the main Truss, and will be a 6500 kg payload, with a power consumption of 2000 W. The verification philosophy is driven by the need to qualify the flight hardware and by the necessary confirmation and correlation of the thermal mathematical models, based on experimental data. The hardware used on AMS-02 is derived from the state-of-the-art ground based detectors for high energy physics, hence not yet proven for operations in vacuum and in extreme thermal environment.

An LHP-based thermal control system for cryocoolers on the Alpha Magnetic Spectrometer (AMS-02) has been designed and analyzed by means of the system level model based on Sinda/Fluint. It is integrated with the International Space Station (ISS) model by means of a time-varying sink temperature definition, and the performance of LHP-based thermal control is investigated. A parametric analysis is carried out also to understand the influence of critical parameters on the operation of LHP.