A novel interference effect in atomic magnetometers is uncovered and characterized, which introduces an important systematic error to greatly deteriorate the accuracy of magnetic‐field measurements (see article number 2000078 by Xinhua Peng and co‐workers). Such an effect provides a first explanation for the phenomenon of asymmetric spectra observed in ultra‐low‐field nuclear magnetic resonance. It is anticipated that the magnetic interference phenomena uncovered here will stimulate interesting new researches in a wide range of magnetometers and their applications in biological, physical, and chemical science.

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Lane change for automated vehicles (AVs) is an important but challenging task in complex dynamic traffic environments. Due to difficulties in guarantee safety as well as a high efficiency, AVs are inclined to choose relatively conservative strategies for lane change. To avoid the conservatism, this paper presents a cooperation-aware lane change method utilizing interactions between vehicles. We first propose an interactive trajectory prediction method to explore possible cooperations between an AV and the others. Further, an evaluation is designed to make a decision on lane change, in which safety, efficiency and comfort are taken into consideration. Thereafter, we propose a motion planning algorithm based on model predictive control (MPC), which incorporates AV's decision and surrounding vehicles' interactive behaviors into constraints so as to avoid collisions during lane change. Quantitative testing results show that compared with the methods without an interactive prediction, our method enhances driving efficiencies of the AV and other vehicles by 14.8% and 2.6% respectively, which indicates that a proper utilization of vehicle interactions can effectively reduce the conservatism of the AV and promote the cooperation between the AV and others.

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