Vehicle trajectory prediction is important for automated vehicles to understand driving scenarios. This paper proposes an encoder-decoder network-based parameterized transfer learning (EDN-PTL) model to predict vehicle trajectory. To improve trajectory prediction accuracy, the motion interaction between the target vehicle and the surrounding vehicles is considered, and a multidimensional spatiotemporal input expansion (MSIA) strategy is proposed to extend the feature dimensions. Additionally, global and local scale features, as well as long and short horizon features, are extracted and used for interactive vehicle trajectory prediction by a CNN and LSTM-based encoder-decoder network (CNN-LSTM-EDN). Moreover, the features extracted by CNN-LSTM-EDN are integrated using a stacked convolutional social pooling network (SCSPN). To enhance the environmental adaptability of the trajectory prediction model, a PTL strategy is proposed to enable transfer learning capabilities of EDN-PTL. Based on the PTL strategy, trajectory prediction accuracy is maintained even when applied to untrained environments. The proposed EDN-PTL model is validated on three types of publicly available naturalistic datasets and compared with several baselines and state-of-the-art (SOTA) methods. The validation results demonstrate that the proposed EDN-PTL achieves better prediction accuracy, robustness, and environmental adaptability compared to the baselines and SOTA methods.

Lane recognition is an important component of autonomous driving system and advanced driving assistance system (ADAS) for intelligent vehicles. In complex driving conditions, accurate and fast lane recognition is a challenging issue. In this paper, a vision-based geometric model (VBGM) is proposed for accurate and fast lane recognition in complex conditions. The framework of the VBGM includes an image preprocessing stage and a lane recognition stage. In the image preprocessing stage, the region of interest (ROI) is extracted from the original image, and the original image is transformed into an undistorted greyscale image. In the lane recognition stage, the lane contour is first extracted using the Roberts operator. Then, to accurately and quickly recognize the lane marking, a lane recognition coordinate system (LRCS) and a rotational LRCS (R-LRCS) are constructed. The distracting contours in abnormal regions are padded based on the LRCS using a contextual frames correlation (CFC) strategy, and the midpoints of the lane contour are identified based on the R-LRCS. Finally, an adaptive-order polynomial fitting model is built to fit the lane marking according to the midpoints in the LRCS. To evaluate the effectiveness of the proposed method, two state-of-the-art methods are selected for comparison. The comparative results indicate that the proposed method possesses a higher recognition rate and speed for lane recognition in complex conditions.

The regulation time is an important index for formation switching control of connected and automated vehicle (CA V) platoon. This paper proposes a time-aware formation control (T AFC) strategy to improve the formation switching performance of CA V platoon. To construct an effective information sharing mechanism among the vehicles in the platoon, a unidirectional weighted graph is designed to construct the relation of the CA V platoon and calculate the impact factor between two different vehicles. Based on the unidirectional weighted graph, the time-aware requirement is converted to the regulation order problem, and the regulation order which corresponding to the minimum time is designed. According to the T AFC, the qualitative regulation strategy of the CA V platoon and the quantitative tune-up strategy of the vehicles are determined. In order to analyze the performance of the TAFC strategy, two state-of-art methods are selected as the benchmarked methods. The validation results demonstrate the proposed method possesses better performance for formation switching control compared with the benchmarked methods.