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In order to track the research progress of AEB-related technologies, this paper makes a systematic analysis and research on the impact factors, key technologies, and effect evaluation of AEB. First, the paper deeply analyzes the three levels of factors affecting the performance of AEB, which are vehicle factors, driver factors, and environmental factors. Second, the paper deeply studies the technical status of the three subsystems of environment perception, decision-making, and control execution. Particularly, the performance of Mazda, Honda, NHTSA, Berkeley, and Seungwuk Moon are compared and analyzed based on MATLAB. Third, the paper summarizes the current AEB virtual test methods, closed field test methods, and its test sites. Three classic evaluation methods in the world, including the AEB test evaluation standards of ENCAP, IIHS, and i-Vista are analyzed. Finally, the paper prospects the specific research directions, including the protection of vulnerable road users, target detection method, collision avoidance strategy, complex scenarios application, and application of emerging technologies. [ABSTRACT FROM AUTHOR]

Traffic energy consumption estimation is significant for the sustainable transportation. However, it is difficult to directly employ macro traffic flow data to accurately estimate the traffic energy consumption due to many traffic energy consumption models need second-by-second vehicle trajectory. To solve this problem, this paper proposes a traffic energy consumption model based on the macro-micro data, which the macro data derived from the fixed-location sensors and sparse micro data derived from the Connected and Automated Vehicles (CAVs). The completed vehicle trajectories are constructed by the nonparametric kernel smoothing algorithm and variational theory. To test the performance of the proposed method, the Next Generation Simulation micro (NGSIM) dataset and Caltrans Performance Measurement System macro dataset obtained from the same road and time are used. The results indicate that the proposed method not only can reflect the characteristics of traffic kinematic waves caused by traffic congestion, but also minimize the errors generated by the macro-micro transformation. In addition, it can significantly improve the accuracy of energy consumption estimation. • Estimation of fuel consumption and emissions is closer to the real values than other methods. • Reconstructing second-by-second vehicle trajectories based on macroscopic traffic data. • Introduces CAVs trajectory data as a reference in the process of estimating the road spatio-temporal speed evolution. • Proposes a spatio-temporal consistency validation framework by using macro and micro data. • Experimental tests the effect of cell size and probe vehicle penetration on reconstruction accuracy. [ABSTRACT FROM AUTHOR]

An accurate vehicle trajectory prediction promotes understanding of the traffic environment and enables task criticality assessment in advanced driver assistance systems (ADASs) in autonomous vehicles and intelligent connected vehicles. Nevertheless, conventional prediction models are characterized by low prediction accuracy, the inability of long-term prediction, and a single-road section adaptation. To tackle these limitations, this study proposes a trajectory prediction model based on a deep encoder–decoder and a deep neural network (DNN). One modification included introducing an attention mechanism into the traditional encoder–decoder framework. Overall, 1794,1400,2100 trajectory samples from highways, intersections, and roundabouts are used to train the proposed framework and obtain optimal deep encoder–decoder architectures for different road section types. Since the experiments revealed no significant advantages of using the attention mechanism in deep encoder–decoder, the mechanism is not included in the optimal architecture. Next, to achieve higher prediction accuracy and better long-term prediction capability, different DNN structures are tested as trajectory correction networks, and the optimal DNN structure is selected. Finally, the experiments are conducted using the proposed deep encoder–decoder framework and the optimal DNN. The results show that the proposed model reaches 92.87%, 86.65%, and 89.15% average trajectory fit ratio (TFR) on a highway, intersection, and a roundabout, respectively. Therefore, the model enables accurate long-term predictions of vehicle trajectories in these road segments. The proposed model and presented results provide a basis for ADASs' trajectory prediction algorithms. [ABSTRACT FROM AUTHOR]