In this paper, the structured trajectory planning of lane change in collision-free road environment is studied and validated using the vehicle-driver integration data, and a new trajectory planning model for lane change is proposed based on linear offset and sine function to balance driver comfort and vehicle dynamics. The trajectory curvature of the proposed model is continuous without mutation, and the zero-based curvature at the starting and end points during lane change assures the motion direction of end points in parallel with the lane line. The field experiment are designed to collect the vehicle-driver integration data, such as steering angle, brake pedal angel and accelerator pedal angel. The correction Correlation analysis of lane-changing maneuver and influencing variables is conducted to obtain the significant variables that can be used to calibrate and test the proposed model. The results demonstrate that vehicle velocity and Y-axis acceleration have significant effects on the lane-changing maneuver, so that the model recalibrated by the samples of different velocity ranges and Y-axis accelerations has better fitted performance compared with the model calibrated by the sample trajectory. In addition, the proposed model presents a decreasing tendency of the lane change trajectory fitted MAE with the increase of time span of calibrating samples at the starting stage. intelligent vehicle, lane change, trajectory planning, vehicle-driver integration Citation: Wang J F, Zhang Q, Zhang Z Q, et al. Structured trajectory planning of collision-free lane change using the vehicle-driver integration data. Sci China Tech Sci, 2016, 59: 825−831,

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An affinity screening and analysis method was established by combining three-dimensional cell bioreactor with HPLC/MS for the active components interacted with cancer cells at simulating the in vivo micro-environment. The differences between the biological fingerprinting chromatograms of HPLC after interacting with live and fixed cells were used to establish a screening recognition model to distinguish bioactive components interacted specifically. Having significant difference (P < 0.05) for model anticancer drugs (paclitaxel and resveratrol) and having no significant difference (P < 0.05) for model non-anticancer drugs (ketoprofen and penicillin G) with model cancer cells demonstrated the feasibility of this recognition model. The model was used to screen bioactive components from Sinopodophyllum hexandrum (Royle) Ying extract. Seven components interacted with Lovo cells were screened. This paper provides a screening and analysis method, mimicking in vivo micro-environment, for bioactive components interacted with cancer cells. This method has the potential to be used in drug discovery programs especially in bioactive components research from traditional Chinese medicines.