Nowadays, with increased sensor perception performance for Advanced Driver Assistance Systems (ADAS), scenariobased simulation is becoming more frequent to manage the complexity of reality in terms of cost and time. The perception system provides the basis for the vehicle guidance algorithms calculation, but the simulation of ADAS sensors is a challenging task in virtual testing. Literature reports the magnitude of relevant modelling approaches and datadriven models becoming increasingly important. A basic method is to fit the sensor output in the virtual environment with highfidelity measurements of realworld scenarios, thus a direct relation can be established between real and synthetic sensor data. To prove the suitability of a method, it is necessary to quantify the gap between simulation and reality to determine the performance of different models. In this work, authors address this problem and visualize the gap by introducing a multilevel evaluation approach that combines Model Generalization Ability Evaluation and Case Implicit Performance Evaluation. The former directly evaluates the model's overall performance, while the latter is used for specific cases in simulation. The study shows that this combined evaluation approach provides an indepth framework for evaluating sensor models to make the differences apparent.

Driver steering intention prediction provides an augmented solution to the design of an onboard collaboration mechanism between human driver and intelligent vehicle. In this study, a multitask sequential learning framework is developed to predict future steering torques and steering postures based on upper limb neuromuscular electromyography signals. The joint representation learning for driving postures and steering intention provides an indepth understanding and accurate modelling of driving steering behaviours. Regarding different testing scenarios, two driving modes, namely, bothhand and singlerighthand modes, are studied. For each driving mode, three different driving postures are further evaluated. Next, a multitask timeseries transformer network (MTSTrans) is developed to predict the future steering torques and driving postures based on the multivariate sequential input and the selfattention mechanism. To evaluate the multitask learning performance and informationsharing characteristics within the network, four distinct twobranch network architectures are evaluated. Empirical validation is conducted through a driving simulatorbased experiment, encompassing 21 participants. The proposed model achieves accurate prediction results on future steering torque prediction as well as driving posture recognition for both twohand and singlehand driving modes. These findings hold significant promise for the advancement of driver steering assistance systems, fostering mutual comprehension and synergy between human drivers and intelligent vehicles.

The progression toward automated driving and the latest advancement in vehicular networking have led to novel and natural humanvehicleroad systems, in which affective humanvehicle interaction is a crucial factor affecting the acceptance, safety, comfort, and traffic efficiency of connected and automated vehicles (CAVs). This development has inspired increasing interest in how to develop affective interaction framework for intelligent cockpit in CAVs. To enable affective humanvehicle interactions in CAVs, knowledge from multiple research areas is needed, including automotive engineering, transportation engineering, humanmachine interaction, computer science, communication, as well as industrial engineering. However, there is currently no systematic survey considering the close relationship between humanvehicleroad and human emotion in the humanvehicleroad coupling process in the CAV context. To facilitate progress in this area, this paper provides a comprehensive literature survey on emotionrelated studies from multiaspects for better design of affective interaction in intelligent cockpit for CAVs. This paper discusses the multimodal expression of human emotions, investigates the human emotion experiment in driving, and particularly emphasizes previous knowledge on human emotion detection, regulation, as well as their applications in CAVs. The promising research perspectives are outlined for researchers and engineers from different research areas to develop CAVs with better acceptance, safety, comfort, and enjoyment for users.

With the rapid development of autonomous vehicles, more and more functions and computing requirements have led to the continuous centralization in the topology of electrical and electronic (E/E) architectures. While certain Tier1 suppliers, such as BOSCH, have previously proposed a serial roadmap for E/E architecture development, implemented since 2015 with significant contributions to the automotive industry, lingering misconceptions and queries persist in actual engineering processes. Notably, there are concerns regarding the perspective of zoneoriented E/E architectures, characterized by zonal concentration, as successors to domainoriented E/E architectures, known for functional concentration. Addressing these misconceptions and queries, this study introduces a novel parallel roadmap for E/E architecture development, concurrently evaluating domainoriented and zoneoriented schemes. Furthermore, the study explores hybrid E/E architectures, amalgamating features from both paradigms. To align with the evolution of E/E architectures, networking technologies must adapt correspondingly. The networking mechanisms pivotal in E/E architecture design are comprehensively discussed. Additionally, the study delves into modeling and verification tools pertinent to E/E architecture topologies. In conclusion, the paper outlines existing challenges and unresolved queries in this domain.