Validation of Vehicle Driving Simulator from Perspective of Velocity and Trajectory Based Driving Behavior under Curve Conditions

With their advantages of high experimental safety, convenient setting of scenes, and easy extraction of control parameters, driving simulators play an increasingly important role in scientific research, such as in road traffic environment safety evaluation and driving behavior characteristics research. Meanwhile, the demand for the validation of driving simulators is increasing as its applications are promoted. In order to validate a driving simulator in a complex environment, curve road conditions with different radii are considered as experimental evaluation scenarios. To attain this, this paper analyzes the reliability and accuracy of the experimental vehicle speed of a driving simulator. Then, qualitative and quantitative analysis of the lateral deviation of the vehicle trajectory is carried out, applying the cosine similarity method. Furthermore, a data-driven method was adopted which takes the longitudinal displacement, lateral displacement, vehicle speed and steering wheel angle of the vehicle as inputs and the lateral offset as the output. Thus, a curve trajectory planning model, a more comprehensive and human-like operation, is established. Based on directional long short-term memory (Bi–LSTM) and a recurrent neural network (RNN), a multiple Bi–LSTM (Mul–Bi–LSTM) is proposed. The prediction performance of LSTM, MLP model and Mul–Bi–LSTM are compared in detail on the validation set and testing set. The results show that the Mul–Bi–LSTM model can generate a trajectory which is very similar to the driver’s curve driving and have a preferable generalization performance. Therefore, this method can solve problems which cannot be realized in real complex scenes in the simulator validation. Selecting the trajectory as the validation parameter can more comprehensively and intuitively reflect the simulator’s curve driving state. Using a speed model and trajectory model instead of a real car experiment can improve the efficiency of simulator validation and lay a foundation for the standardization of simulator validation.

Simulation in vocational education

This study addresses the use of driving simulators in vocational education. The aim of the study is to use the experiences of vocational teachers and questions to identify critical aspects of simulator-assisted teaching. The Background section contains a description of studies regarding digitalisation within other educational contexts and teaching with simulators in other contexts as well as teacher competencies and the school’s quality measures. The study’s empirical evidence consists of observations and discussions with vocational teachers. The Results section contains an account of the vocational teacher’s questions in the form of identified problem areas. The Conclusions section of the study highlights simulator-assisted teaching and the importance of substantive aspects, as well as the connection to professional knowledge and competency, the possibility of adapting the teaching based on the needs of the student, and the consequences of the teaching for the professional skills and knowledge acquired by the student. The Discussion section addresses aspects such as teaching-related issues regarding transfer, the work of the vocational programme and the school with regard to the implementation of new technology in the teaching process, and the vocational teacher’s role and situation. In order to further develop knowledge about simulator-supported teaching in vocational education, more practice related studies of students' learning process and how the teaching contributes to the development of vocational skills are required.

Assessing the contribution of active somatosensory stimulation to self-acceleration perception in dynamic driving simulators

In dynamic driving simulators, the experience of operating a vehicle is reproduced by combining visual stimuli generated by graphical rendering with inertial stimuli generated by platform motion. Due to inherent limitations of the platform workspace, inertial stimulation is subject to shortcomings in the form of missing cues, false cues, and/or scaling errors, which negatively affect simulation fidelity. In the present study, we aim at quantifying the relative contribution of an active somatosensory stimulation to the perceived intensity of self-motion, relative to other sensory systems. Participants judged the intensity of longitudinal and lateral driving maneuvers in a dynamic driving simulator in passive driving conditions, with and without additional active somatosensory stimulation, as provided by an Active Seat (AS) and Active Belts (AB) integrated system (ASB). The results show that ASB enhances the perceived intensity of sustained decelerations, and increases the precision of acceleration perception overall. Our findings are consistent with models of perception, and indicate that active somatosensory stimulation can indeed be used to improve simulation fidelity.

A Systematic Review of Augmented Reality Applications for Automated Driving: 2009–2020

While augmented reality (AR) interfaces have been researched extensively over the last decades, studies on their application in vehicles have only recently advanced. In this paper, we systematically review 12 years of AR research in the context of automated driving (AD), from 2009 to 2020. Due to the multitude of possibilities for studies with regard to AR technology, at present, the pool of findings is heterogeneous and non-transparent. From a review of the literature we identified N = 156 papers with the goal to analyze the status quo of existing AR studies in AD, and to classify the related literature into application areas. We provide insights into the utilization of AR technology used at different levels of vehicle automation, and for different users (drivers, passengers, pedestrians) and tasks. Results show that most studies focused on safety aspects, driving assistance, and designing non-driving related tasks. AR navigation, trust in automated vehicles (AVs), and interaction experiences also marked a significant portion of the published papers, however a wide range of different parameters was investigated by researchers. Among other things, we find that there is a growing trend toward simulating AR content within virtual driving simulators. We conclude with a discussion of open challenges, and give recommendation for future research in automated driving at the AR side of the reality-virtuality continuum.

Chauffeur: Benchmark Suite for Design and End-to-End Analysis of Self-Driving Vehicles on Embedded Systems

Self-driving systems execute an ensemble of different self-driving workloads on embedded systems in an end-to-end manner, subject to functional and performance requirements. To enable exploration, optimization, and end-to-end evaluation on different embedded platforms, system designers critically need a benchmark suite that enables flexible and seamless configuration of self-driving scenarios, which realistically reflects real-world self-driving workloads’ unique characteristics. Existing CPU and GPU embedded benchmark suites typically (1) consider isolated applications, (2) are not sensor-driven, and (3) are unable to support emerging self-driving applications that simultaneously utilize CPUs and GPUs with stringent timing requirements. On the other hand, full-system self-driving simulators (e.g., AUTOWARE, APOLLO) focus on functional simulation, but lack the ability to evaluate the self-driving software stack on various embedded platforms. To address design needs, we present Chauffeur, the first open-source end-to-end benchmark suite for self-driving vehicles with configurable representative workloads. Chauffeur is easy to configure and run, enabling researchers to evaluate different platform configurations and explore alternative instantiations of the self-driving software pipeline. Chauffeur runs on diverse emerging platforms and exploits heterogeneous onboard resources. Our initial characterization of Chauffeur on different embedded platforms – NVIDIA Jetson TX2 and Drive PX2 – enables comparative evaluation of these GPU platforms in executing an end-to-end self-driving computational pipeline to assess the end-to-end response times on these emerging embedded platforms while also creating opportunities to create application gangs for better response times. Chauffeur enables researchers to benchmark representative self-driving workloads and flexibly compose them for different self-driving scenarios to explore end-to-end tradeoffs between design constraints, power budget, real-time performance requirements, and accuracy of applications.