Driving Intention Recognition of Surrounding Vehicles Based on a Time-Sequenced Weights Hidden Markov Model for Autonomous Driving.

Accurate perception, especially situational awareness, is central to the evolution of autonomous driving. This necessitates understanding both the traffic conditions and driving intentions of surrounding vehicles. Given the unobservable nature of driving intentions, the hidden Markov model (HMM) has emerged as a popular tool for intention recognition, owing to its ability to relate observable and hidden variables.

Synthesis of the bismuth oxyhalide solid solutions with tunable band gap and photocatalytic activities.

Three series of BiOM(x)R(1-x) (M, R = Cl, Br, I) solid solutions were systematically synthesized through a low-temperature precipitation. These solid solutions were characterized by XRD, FESEM, TEM, EDS, UV-vis spectra, nitrogen sorption/desorption, and PL. The tunable band gaps of the as-prepared solid solutions were realized via only changing the molar ratio of two halide ions.

One-pot solvothermal synthesis of uniform layer-by-layer self-assembled ultrathin hexagonal Gd2O2S nanoplates and luminescent properties from single doped Eu3+ and codoped Er3+, Yb3+.

Uniform Gd(2)O(2)S flower-like nano-assemblies were prepared through one-pot mild solvothermal synthesis. The parallel nanoplates are the building blocks, ∼3 nm in thickness and 20-30 in diameter. Ethanediamine, the main solvent, plays an important role in dissolving a large amount of sulphur and producing active S(2-) ions, which results in the direct formation of Gd(2)O(2)S.