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For the purposes of on-line control, e.g., in an automatic driving system, or of closed-loop directional control simulation, an optimal preview artificial neural network (ANN) driver model based on error elimination algorithm(EEA) is built. Then the optimal preview times are discussed in high frequency range in this system. The simulation results of optimal preview ANN driver model and Error Elimination Algorithm driver model are compared under the condition of different vehicle speeds and paths, which shows that the proposed approach is efficient and reliable enough, particularly for driver-vehicle closed-loop system.

Owing to the stochastic nature of engine knock, determination of the knock limited spark angle (KLSA) is difficult in engine cycle simulation. Therefore, the state-of-the-art knock modeling is mostly limited to either merely predicting knock onset (i.e. auto-ignition of end gas) or combining a simple unburned mass fraction (UMF) model representative of knock intensity (KI). In this study, a newly developed KLSA model, which takes both predictions of knock onset and intensity into account, is firstly introduced. Multiple variables including the excess air ratio, EGR ratio, cylinder pressure and the end gas temperature are included in the knock onset model. Based on the auto-ignition theory of hot spots in end gas, both the energy density and heat release rate in hot spots are taken into consideration in the KI model.

In partially automated and conditionally automated vehicles, a part of the work of human drivers is replaced by the system, and the main source of safety risks is no longer system failures, but non-failure risks caused by insufficient system function design. The absence of unreasonable risk due to hazards resulting from functional insufficiencies of the intended functionality or by reasonably foreseeable misuse by persons, is referred to as the Safety Of The Intended Functionality. Drivers have the responsibility to supervise the automated driving system. When they don't agree with the operation behavior of the system, they will interfere with the instructions. However, this may lead to potential risks.

Visual Place Recognition (VPR) excels at providing a good location prior for autonomous vehicles to initialize the map-based visual SLAM system, especially when the environment changes after a long term. Condition change and viewpoint change, which influences features extracted from images, are two of the major challenges in recognizing a visited place. Existing VPR methods focus on developing the robustness of global feature to address them but ignore the benefits that local feature can auxiliarily offer. Therefore, we introduce a novel hierarchical place recognition method with both global and local features deriving from homologous VLAD to improve the VPR performance. Our model is weak supervised by GPS label and we design a fine-tuning strategy with a coupled triplet loss to make the model more suitable for extracting local features.

In this paper an estimation method on location of peak pressure (LPP) employing flame ionization measurement, with the spark plug as a sensor, was discussed to achieve combustion parameters estimation at quick start of HEV engines. Through the cycle-based ion signal analysis, the location of peak pressure can be extracted in individual cylinder for the optimization of engine quick start control of HEV engine. A series of quick start processes with different cranking speed and engine coolant temperature are tested for establishing the relationship between the ion signals and the combustion parameters. An Artificial Neural Network (ANN) algorithm is used in this study for estimating these two combustion parameters. The experiment results show that the location of peak pressure can be well established by this method.

In order to meet increasingly stringent emission regulations, it is significance to establish a control- oriented transient NOx and PM emission prediction model and improve the accuracy and real-time performance. In this study, the prediction model of transient PM emissions based on Transformer is established. In terms of model accuracy and real-time performance, Transformer emission prediction model is compared with Multilayer perceptron (MLP) neural network and Long-Short Term Memory (LSTM) emission prediction model. The results show that the performance of Transformer transient emission prediction model is superior to other model structures, it can be used for real-time prediction.