The autonomous vehicles (AVs) need to share the driving environment with the human driving vehicles (HDVs) on expressway in the future. The non-humanlike lane changing (LC) behavior of AVs can mislead human drivers, which brings potential risks. Stronger humanlike ability requires a more complex algorithm. However, the requirement of the on-board vehicle computation resources limits the humanlike ability of LC algorithms. In this context, considering environmental risks, driver speed requirements and driver focus shifting process, this paper proposes a new type of LC algorithm based on artificial potential field (APF) aiming to improve the humanlike ability. The coupling relationship between the longitudinal and the lateral potential field forces is analyzed theoretically based on environmental risk APF. Based on the relationship, we proposed a conversion mechanism of the driver between the lateral and longitudinal potential field forces to mimic the driver's speed requirement. Then a target lane selection strategy is designed to trade-off the driver's speed requirement and safety between multiple lanes. Finally, to mimic the driver's focus transfer process, the lateral forces of the ego vehicle are analyzed theoretically to further propose a moving virtual lane lines algorithm to build the lateral space varying potential field. The algorithm proposed in this paper is validated by comparing with other LC algorithms in the real traffic scenarios. The results indicate that the proposed algorithm has a better ability to mimic human driver's LC decision-making and provides a smoother and safer humanlike trajectory.INDEX TERMS Autonomous vehicles, artificial potential field, lane changing decision-making, motion planning.

In this study, we discovered that a certain concentration of Na+ (15 mM) significantly improved the bond strength (12.94 ± 0.93 MPa), thermal stability (72.68 °C), rheological properties, and textural attributes of walnut protein isolate (WNPI)-κ-carrageenan (KC) composite gel. Electrostatic force, hydrophobic interaction, hydrogen bond, and disulfide bond were also significantly strengthened; the α-helix decreased, and the β-sheet increased in the secondary structure, indicating that the protein molecules in the gel system aggregated in an orderly manner, which led to a much denser and more uniform gel network as well as improved water-holding capacity. In this experimental research, we developed a new type of walnut protein gel that could provide technical support for the high-value utilization and quality control of walnut protein.

In this paper, a car-following model considering the preceding vehicle type is proposed to describe the longitudinal driving behavior closer to reality. Based on the naturalistic driving data sampled in real traffic for more than half a year, the relation between ego vehicle velocity and relative distance was analyzed by a multi-variable Gaussian Mixture model, from which it is found that the driver following behavior is influenced by the type of leading vehicle. Then a Hidden Markov model was designed to identify the vehicle type. This car-following model was trained and tested by using the naturalistic driving data. It can identify the leading vehicle type, i.e., passenger car, bus, and truck, and predict the ego vehicle velocity and relative distance based on a series of limited historical data in real time. The experimental validation results show that the identification accuracy of vehicle type under the static and dynamical conditions are 96.6% and 83.1%, respectively. Furthermore, comparing the results with the well-known collision avoidance model and intelligent driver model show that this new model is more accurate and can be used to design advanced driver assist systems for better adaptability to traffic conditions.

Summary Heat processing has a significant effect on the flavour of walnut kernels, while the related analyses on their characteristic flavour changes have not been extensively studied yet. In this study, headspace solid phase micro‐extraction combined with gas chromatography–mass spectrometry‐olfactometry (HS‐SPME‐GC–MS‐O), electronic nose (E‐nose) and multivariate analysis methods were used to explore the effects of microwave, baking and drying treatments on the characteristic flavour of walnut kernels. The results showed that the content of aldehydes and heterocyclic compounds increased significantly after heat treatment. A total of eighty‐nine volatile compounds were identified by the above three heat processing methods, mainly aldehydes and alkanes, including twenty‐five aroma active substances and twelve potential markers. It also discovered that the flavour characteristics of walnut kernels changed from green to roasted and burnt odours after heat treatment, in which the microwave treatment group displayed the strongest roasted and burnt flavours among the above processing methods. The comparative analysis of the differences in characteristic flavour on walnut kernels via different heat processing methods could provide a technical support for the development of walnut products and their industrial utilisation.

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