Realistic 3D Simulators for Automotive: A Review of Main Applications and Features.

Recent advancements in vehicle technology have stimulated innovation across the automotive sector, from Advanced Driver Assistance Systems (ADAS) to autonomous driving and motorsport applications. Modern vehicles, equipped with sensors for perception, localization, navigation, and actuators for autonomous driving, generate vast amounts of data used for training and evaluating autonomous systems. Real-world testing is essential for validation but is complex, expensive, and time-intensive, requiring multiple vehicles and reference systems. To address these challenges, computer graphics-based simulators offer a compelling solution by providing high-fidelity 3D environments to simulate vehicles and road users. These simulators are crucial for developing, validating, and testing ADAS, autonomous driving systems, and cooperative driving systems, and enhancing vehicle performance and driver training in motorsport. This paper reviews computer graphics-based simulators tailored for automotive applications. It begins with an overview of their applications and analyzes their key features. Additionally, this paper compares five open-source (CARLA, AirSim, LGSVL, AWSIM, and DeepDrive) and ten commercial simulators. Our findings indicate that open-source simulators are best for the research community, offering realistic 3D environments, multiple sensor support, APIs, co-simulation, and community support. Conversely, commercial simulators, while less extensible, provide a broader set of features and solutions.

Driving assistant using generative AI pre-generated messages in simulator-based driving assessment: A step towards low-cost simulator-based driving assessment.

This paper presents a novel approach for a low-cost simulator-based driving assessment system incorporating a speech-based assistant, using pre-generated messages from Generative AI to achieve real-time interaction during the assessment. Simulator-based assessment is a crucial apparatus in the research toolkit for various fields. Traditional assessment approaches, like on-road evaluation, though reliable, can be risky, costly, and inaccessible. Simulator-based assessment using stationary driving simulators offers a safer evaluation and can be tailored to specific needs. However, these simulators are often only available to research-focused institutions due to their cost. To address this issue, our study proposes a system with the aforementioned properties aiming to enhance drivers' situational awareness, and foster positive emotional states, i.e., high valence and medium arousal, while assessing participants to prevent subpar performers from proceeding to the next stages of assessment and/or rehabilitation. In addition, this study introduces the speech-based assistant which provides timely guidance adaptable to the ever-changing context of the driving environment and vehicle state. The study's preliminary outcomes reveal encouraging progress, highlighting improved driving performance and positive emotional states when participants are engaged with the assistant during the assessment.

Long-acting prescriptions and therapy for HIV-1 from market launch to the present in Germany (May 2021 to December 2023).

Background: In Europe, the combination of cabotegravir (CAB) with rilpivirine (RPV) has been approved as a dual injection long-acting (LA) therapy for the treatment of human immunodeficiency virus type 1 (HIV-1) infections in adults since December 2020. Studies have shown that between 36 and 61% of people living with HIV (PLWHIV) prefer LA therapy. However, there are no real-world data on the number of people receiving LA therapy, in Germany or internationally. The aim of this study was to assess the current situation and trends in usage of LA therapy for the treatment of HIV-1 in Germany. Methods: Based on pharmacy prescription data derived from Insight Health, the monthly number of prescriptions for oral CAB, CAB-LA, and RPV-LA over the entire period of availability in Germany was analyzed and evaluated (May 2021 to December 2023). The number of 1st and 2nd initiation injections and subsequent maintenance injections was calculated on the basis of the prescriptions for oral CAB initiation. Results: The bimonthly schedule resulted in two growing cohorts from September 2021 with an estimated 14,523 CAB-LA prescriptions over the entire period. Accordingly, in December 2023, there were approximately 1,364 PLWHIV receiving LA therapy, of whom 1,318 were receiving maintenance therapy. Only treatments with bimonthly regimens were carried out. Accounting for people not covered by statutory health insurance (~13%), a total of ~1,600 PLWHIV were receiving LA therapy in Germany in December 2023. The average rounded annual cost of therapy in 2023 was €11,940 (maintenance therapy with initiation) and €10,950 (maintenance therapy without initiation). Conclusion: To our knowledge, this is the first study of real-world use and number of people receiving LA therapy. A strength of our study is the nearly complete coverage of people with statutory health insurance in Germany. The predicted demand for LA therapy does not match the actual number of people receiving LA therapy. Although the number of PLWHIV receiving LA therapy increased steadily, they accounted for just under 2% of the estimated total number of people receiving HIV therapy in Germany in 2023, almost 2 years after the market launch. No significant increase in prescriptions is expected; on the contrary, the trend is leveling off and is unlikely to change drastically in the near future. Hence, the need for this mode of therapy in Germany appears to be limited. Follow-up studies at regular intervals on the further course would be useful and are recommended, as well as investigations into the possible reasons for the slow uptake to inform public health experts and possibly broaden treatment options.

Integrating Modular Pipelines with End-to-End Learning: A Hybrid Approach for Robust and Reliable Autonomous Driving Systems.

Autonomous driving navigation relies on diverse approaches, each with advantages and limitations depending on various factors. For HD maps, modular systems excel, while end-to-end methods dominate mapless scenarios. However, few leverage the strengths of both. This paper innovates by proposing a hybrid architecture that seamlessly integrates modular perception and control modules with data-driven path planning. This innovative design leverages the strengths of both approaches, enabling a clear understanding and debugging of individual components while simultaneously harnessing the learning power of end-to-end approaches. Our proposed architecture achieved first and second place in the 2023 CARLA Autonomous Driving Challenge's SENSORS and MAP tracks, respectively. These results demonstrate the architecture's effectiveness in both map-based and mapless navigation. We achieved a driving score of 41.56 and the highest route completion of 86.03 in the MAP track of the CARLA Challenge leaderboard 1, and driving scores of 35.36 and 1.23 in the CARLA Challenge SENSOR track with route completions of 85.01 and 9.55, for, respectively, leaderboard 1 and 2. The results of leaderboard 2 raised the hybrid architecture to the first position, winning the edition of the 2023 CARLA Autonomous Driving Competition.

Novel model predictive control-based motion cueing algorithm for compensating centrifugal acceleration in KUKA robocoaster-based driving simulators.

The washout motion cueing algorithm (MCA) is a critical element in driving simulators, designed to faithfully reproduce precise motion cues while minimizing false cues during simulation processes, particularly deceptive translational and rotational cues. To enhance motion sensation accuracy and optimize the use of available workspace, model predictive control (MPC) has been employed to develop innovative motion cueing algorithms. While most MCAs have been tailored for the Steward motion platform, there has been a recent adoption of the motion platform based on KUKA Robocoaster as an economical option for driving simulators. However, leveraging the full potential of the KUKA Robocoaster requires trajectory conversion of the motion base. Thus, this research proposes a novel MCA specifically designed for the KUKA Robocoaster-based motion platform, utilizing large planar circular motion to simulate lateral movement for drivers. Nonetheless, circular motion introduces disruptive centrifugal forces, which can be mitigated through proper pitch-tilted angles. The novel MPC generates simulated motion that accurately follows the lateral specific force target and effectively maintains the roll angular velocity below its threshold value. Additionally, it compensates for disturbing centrifugal acceleration by implementing pitch rotational motion, ensuring the pitch angular velocity remains below its threshold. Simulation tasks conducted on the motion platform, focusing solely on lateral acceleration, demonstrate the successful elimination of false motion cues in both the roll/sway and pitch/surge channels. The proposed innovative MPC solution offers an original approach to motion cueing algorithms in KUKA Robocoaster-based driving simulators. It enables the exploitation of the KUKA Robocoaster platform's capabilities while delivering accurate and immersive motion cues to drivers during simulation experiences.

Carbohydrate larval antigen (CarLA IgA) responses to mixed species nematode infection in pasture grazed Angora goats.

The genetics of indicator traits for resistance of Angora goats to gastrointestinal nematode parasite infections, and their relationships with productivity traits, were investigated on a commercial mixed-enterprise farm in the eastern North Island of New Zealand. Faecal egg counts (FEC), specific Immunoglobulin A (IgA) and Immunoglobulin G (IgG) antibody titres against carbohydrate larval antigen (CarLA) in saliva, live weight and fleece weights were recorded from 278 goats of 19-20 months of age, run as four separate mobs (breeding bucks, castrated males (wethers), or 2 groups of breeding does). Summary statistics showed the mobs differed significantly in liveweight, loge (FEC+50), loge (IgA) and loge (IgG). Genetic parameters were estimated using an animal model with repeated records where appropriate, after adjusting for the different contemporary animal groups, using the restricted maximum likelihood (REML) package. Heritability estimates from repeated measures were 0.19 ± 0.16 for FEC, 0.28 ± 0.16 for CarLA specific IgA and 0.23 ± 0.15 for CarLA specific IgG. The CarLA specific IgA response was negatively genetically correlated with FEC (-0.99 ± 0.31) suggesting that it could be used as a selection tool for breeding resistant animals. Although the genetic and phenotypic correlations between CarLA IgA and IgG were high and significant, the analysis between loge (FEC+50) and loge CarLA IgG did not converge. Further, both FEC and CarLA IgA showed significant and favourable genetic correlations with live weight. In contrast, CarLA IgG showed an unfavourable phenotypic correlation with liveweight. While this is only a preliminary study, the results do suggest that the immunoassay measuring salivary CarLA IgA response may have utility as a selection tool for parasite resistance in some breeds of goats.

Mitigating Cancer Disparities Through an Innovative Navigation Program.

The Office of Cancer Health Equity at the Atrium Health Wake Forest Baptist Comprehensive Cancer Center used a community-engaged approach to develop an innovative Population Health Navigation Program designed to improve access to cancer care and reduce cancer disparities.

Driver Take-Over Behaviour Study Based on Gaze Focalization and Vehicle Data in CARLA Simulator.

Autonomous vehicles are the near future of the automobile industry. However, until they reach Level 5, humans and cars will share this intermediate future. Therefore, studying the transition between autonomous and manual modes is a fascinating topic. Automated vehicles may still need to occasionally hand the control to drivers due to technology limitations and legal requirements. This paper presents a study of driver behaviour in the transition between autonomous and manual modes using a CARLA simulator. To our knowledge, this is the first take-over study with transitions conducted on this simulator. For this purpose, we obtain driver gaze focalization and fuse it with the road's semantic segmentation to track to where and when the user is paying attention, besides the actuators' reaction-time measurements provided in the literature. To track gaze focalization in a non-intrusive and inexpensive way, we use a method based on a camera developed in previous works. We devised it with the OpenFace 2.0 toolkit and a NARMAX calibration method. It transforms the face parameters extracted by the toolkit into the point where the user is looking on the simulator scene. The study was carried out by different users using our simulator, which is composed of three screens, a steering wheel and pedals. We distributed this proposal in two different computer systems due to the computational cost of the simulator based on the CARLA simulator. The robot operating system (ROS) framework is in charge of the communication of both systems to provide portability and flexibility to the proposal. Results of the transition analysis are provided using state-of-the-art metrics and a novel driver situation-awareness metric for 20 users in two different scenarios.

Path-Tracking Control Strategy of Unmanned Vehicle Based on DDPG Algorithm.

This paper proposes a deep reinforcement learning (DRL)-based algorithm in the path-tracking controller of an unmanned vehicle to autonomously learn the path-tracking capability of the vehicle by interacting with the CARLA environment. To solve the problem of the high estimation of the Q-value of the DDPG algorithm and slow training speed, the controller adopts the deep deterministic policy gradient algorithm of the double critic network (DCN-DDPG), obtains the trained model through offline learning, and sends control commands to the unmanned vehicle to make the vehicle drive according to the determined route. This method aimed to address the problem of unmanned-vehicle path tracking. This paper proposes a Markov decision process model, including the design of state, action-and-reward value functions, and trained the control strategy in the CARLA simulator Town04 urban scene. The tracking task was completed under various working conditions, and its tracking effect was compared with the original DDPG algorithm, model predictive control (MPC), and pure pursuit. It was verified that the designed control strategy has good environmental adaptability, speed adaptability, and tracking performance.

Comparison of Pedestrian Detectors for LiDAR Sensor Trained on Custom Synthetic, Real and Mixed Datasets.

Deep learning algorithms for object detection used in autonomous vehicles require a huge amount of labeled data. Data collecting and labeling is time consuming and, most importantly, in most cases useful only for a single specific sensor application. Therefore, in the course of the research which is presented in this paper, the LiDAR pedestrian detection algorithm was trained on synthetically generated data and mixed (real and synthetic) datasets. The road environment was simulated with the application of the 3D rendering Carla engine, while the data for analysis were obtained from the LiDAR sensor model. In the proposed approach, the data generated by the simulator are automatically labeled, reshaped into range images and used as training data for a deep learning algorithm. Real data from Waymo open dataset are used to validate the performance of detectors trained on synthetic, real and mixed datasets. YOLOv4 neural network architecture is used for pedestrian detection from the LiDAR data. The goal of this paper is to verify if the synthetically generated data can improve the detector's performance. Presented results prove that the YOLOv4 model trained on a custom mixed dataset achieved an increase in precision and recall of a few percent, giving an F1-score of 0.84.

Improving Semantic Segmentation of Urban Scenes for Self-Driving Cars with Synthetic Images.

Semantic segmentation of an incoming visual stream from cameras is an essential part of the perception system of self-driving cars. State-of-the-art results in semantic segmentation have been achieved with deep neural networks (DNNs), yet training them requires large datasets, which are difficult and costly to acquire and time-consuming to label. A viable alternative to training DNNs solely on real-world datasets is to augment them with synthetic images, which can be easily modified and generated in large numbers. In the present study, we aim at improving the accuracy of semantic segmentation of urban scenes by augmenting the Cityscapes real-world dataset with synthetic images generated with the open-source driving simulator CARLA (Car Learning to Act). Augmentation with synthetic images with a low degree of photorealism from the MICC-SRI (Media Integration and Communication Center-Semantic Road Inpainting) dataset does not result in the improvement of the accuracy of semantic segmentation, yet both MobileNetV2 and Xception DNNs used in the present study demonstrate a better accuracy after training on the custom-made CCM (Cityscapes-CARLA Mixed) dataset, which contains both real-world Cityscapes images and high-resolution synthetic images generated with CARLA, than after training only on the real-world Cityscapes images. However, the accuracy of semantic segmentation does not improve proportionally to the amount of the synthetic data used for augmentation, which indicates that augmentation with a larger amount of synthetic data is not always better.

Navigating an Automated Driving Vehicle via the Early Fusion of Multi-Modality.

The ability of artificial intelligence to drive toward an intended destination is a key component of an autonomous vehicle. Different paradigms are now being employed to address artificial intelligence advancement. On the one hand, modular pipelines break down the driving model into submodels, such as perception, maneuver planning and control. On the other hand, we used the end-to-end driving method to assign raw sensor data directly to vehicle control signals. The latter is less well-studied but is becoming more popular since it is easier to use. This article focuses on end-to-end autonomous driving, using RGB pictures as the primary sensor input data. The autonomous vehicle is equipped with a camera and active sensors, such as LiDAR and Radar, for safe navigation. Active sensors (e.g., LiDAR) provide more accurate depth information than passive sensors. As a result, this paper examines whether combining the RGB from the camera and active depth information from LiDAR has better results in end-to-end artificial driving than using only a single modality. This paper focuses on the early fusion of multi-modality and demonstrates how it outperforms a single modality using the CARLA simulator.

Operational design domain of automated vehicles for crossing maneuvers at two-way stop-controlled intersections.

The departure sight triangle provides the view for the vehicle waiting to cross at the two-way stop-controlled intersection. The factors influencing the sight triangle for human drivers are considered in the 2018 AASHTO Green Book, but the Green Book lacks quantitative estimations for automated vehicles (AVs). Therefore, to guarantee the AV's operational safety, this study investigated the impact of intersection angle, speed, and crossing distance on the AV's intersection crossing maneuver. Using physics theorems and cosine law, formulae for the detecting angle (DA) and distance (DD), the two main components of the departure sight triangle, were developed for the acute- and obtuse-angle sides of the intersection for an AV approaching on the minor road; the minimum required DA and DD, with a given crossing distance, are thus proposed for the AV's operational design domain (ODD). Calculations indicate that the DD is mainly affected by the major road design speed and crossing distance, and that the DD increases very quickly as the speed and crossing distance increase. The intersection angle was found to have great impact on the DA on both the acute and obtuse sides, but its influence is negative on the acute side and positive on the obtuse side. On the acute side, the ODD detecting angle range is set as [83.4, 132.7], [80.7, 131.6], and [78.4, 130.7] degrees for major roads with 2, 4, and 6 lanes, respectively. On the obtuse side, the ODD is set as [57.4, 160.6], [70.6, 207.9], and [82.2, 249.1] m for the same respective roads. After comparing the DA and DD results, and depending on the intersection design attributes, it is concluded that most engineering attention should be paid to the DA on the acute side and DD on the obtuse side.

Dual-flow network with attention for autonomous driving.

We present a dual-flow network for autonomous driving using an attention mechanism. The model works as follows: (i) The perception network extracts red, blue, and green (RGB) images from the video at low speed as input and performs feature extraction of the images; (ii) The motion network obtains grayscale images from the video at high speed as the input and completes the extraction of object motion features; (iii) The perception and motion networks are fused using an attention mechanism at each feature layer to perform the waypoint prediction. The model was trained and tested using the CARLA simulator and enabled autonomous driving in complex urban environments, achieving a success rate of 74%, especially in the case of multiple dynamic objects.

Multisensory Testing Framework for Advanced Driver Assistant Systems Supported by High-Quality 3D Simulation.

ADAS and autonomous technologies in vehicles become more and more complex, which increases development time and expenses. This paper presents a new real-time ADAS multisensory validation system, which can speed up the development and implementation processes while lowering its cost. The proposed test system integrates a high-quality 3D CARLA simulator with a real-time-based automation platform. We present system experimental verifications on several types of sensors and testing system architectures. The first, open-loop experiment explains the real-time capabilities of the system based on the Mobileye 6 camera sensor detections. The second experiment runs a real-time closed-loop test of a lane-keeping algorithm (LKA) based on the Mobileye 6 line detection. The last experiment presents a simulation of Velodyne VLP-16 lidar, which runs a free space detection algorithm. Simulated lidar output is compared with the real lidar performance. We show that the platform generates reproducible results and allows closed-loop operation which, combined with a real-time collection of event information, promises good scalability toward complex ADAS or autonomous functionalities testing.

Stress-Induced Immunomodulation in Low and High Reactive Sheep.

The objective of this study was to investigate the relationship between stress and temperament on the humoral immune response of ewes. Eighty ewes were allocated to one of four treatment groups in a 2 × 2 factorial design (n = 20 ewes/treatment): low (LR) and high (HR) reactive ewes were either exposed to no stress (CON) or were visually isolated (STRESS). Ewes remained in treatment pens for 23 h: heart rate was measured continuously, and saliva samples were collected prior to testing and at 0.5 h and 23 h for measurement of cortisol, CarLA IgA and total IgA concentrations. After the first 0.5 h, heart rate was elevated, and cortisol concentrations tended to be higher, whereas CarLa IgA concentrations were lower in STRESS than CON ewes. Similarly, after 23 h, cortisol concentrations remained elevated and CarLA IgA concentrations remained lower in STRESS than CON ewes. Interestingly, total IgA concentrations were not influenced by a 0.5 h or 23 h stressor. Overall, CarLA IgA concentrations were lower in HR than LR ewes at 0.5 h, but there was no significant stress × temperament interaction. Therefore, stress appears to have an immunosuppressive effect on CarLA IgA but not total IgA concentrations in ewes.

Serum dietary fatty acids and coronary heart disease risk - A nested case-control-study within the CARLA cohort.

BACKGROUND AND AIMS: Diet is known to play a decisive role in the development of coronary heart disease (CHD). One factor believed to decrease lifetime risk of CHD is the consumption of omega-3 fatty acids. Yet, conclusive evidence regarding the potential cardioprotective effects of fatty acids is far from being reached. The present study aimed to provide further evidence on the association of serum fatty acid profiles with CHD risk. METHODS AND RESULTS: The CARdio-vascular Disease, Living and Ageing in Halle study (CARLA study) is an observational cohort study comprising an older adult's general population with a high level of cardiovascular risk factors. In a matched case-control design the serum fatty acid concentrations of 73 subjects with an incident fatal or nonfatal CHD event were compared to 146 controls matched for sex and age. Our data show that the participants of the CARLA study are underserved in unsaturated fatty acids with respect to current dietary recommendations. In addition, the ratio of omega-6 to omega-3 fatty acids was determined to be 8:1 which underlines the consumption of a Western-style diet enriched in omega-6 fatty acids. There were no significant differences in fatty acid patterns between cases and controls. Thus, no clear association of particular serum fatty acid levels with cardiovascular risk was found. CONCLUSION: Our results support the conclusion that in populations with a homogenous low level of omega-3 polyunsaturated fatty acids consumption, serum fatty acid levels are not associated with CHD risk.

Significance of anti-CarLA salivary IgA antibody in first grazing season cattle naturally infected with gastrointestinal nematodes.

A carbohydrate larval surface antigen (CarLA) present on infective larvae of all trichostrongylid nematodes is a target antigen for host immunoglobulins (Ig). Levels of anti-CarLA salivary IgA antibody (CarLA-IgA) have been shown to be correlated to the level of protective immunity to GIN in sheep and deer but no information is available in cattle. The first objective of this study was to assess the pattern of CarLA-IgA response in 7 groups (G1-G7) of first grazing season cattle (FGSC) naturally infected with gastrointestinal nematodes. The second objective was to assess the phenotypic correlations between CarLA-IgA level, 3 parasitological indicators (faecal egg count-FEC, pepsinogen level, serum anti-O. ostertagi IgG antibody level-OstertagiaIgG), a clinical indicator (diarrhea score) and average daily weight gain (ADWG). Overall, CarLA-IgA response gradually increased over grazing season and showed large variations in speed and magnitude both between and within groups. Based on the mean group CarLA-IgA response pattern, the 7 groups could be allocated to 3 different classes: (i) 'Late High' class characterized by a high response at housing (G1 and G2); (ii) 'Low' class with a low response over time (G3, G4 and G5); and (iii) 'Early' class with an high initial then stable response (G6 and G7). This classification was consistent with the grazing management practices. In the 'Late High' class, the mean CarLA-IgA at housing was 6.05units/mL and negatively correlated with FEC while no correlation was seen with the other indicators nor ADWG. In the 'Low' class, CarLA response at housing was low (1.95units/mL) and mainly positively correlated with OstertagiaIgG. In the 'Early' class, mean CarLA-IgA ranged from 1.32 to 1.86units/mL during the grazing season and positive correlations were seen with parasitological and clinical indicators. These results suggest that, according to the intensity of larval challenge occurring during the first grazing season, CarLA-IgA response in cattle could be either an indicator of the early manifestation of immunity (FEC decreases) or the reflection of exposure to GIN.

Observations on the phenotypic relationships between anti-CarLA salivary IgA antibody response, nematode infection levels and growth rates in farmed red (Cervus elaphus) and wapiti hybrid deer (Cervus elaphus canadensis).

Nematode parasites are one of the most significant production limiting factors in farmed deer in New Zealand. One long term strategy to reduce reliance on anthelmintics is to select deer that develop resistance to parasites. It has been shown in sheep that secretory antibody (IgA) in the saliva against a Carbohydrate Larval Antigen (CarLA) on infective larvae (L3) of a wide range of gastro-intestinal nematodes protects against reinfection. This paper describes a longitudinal slaughter study undertaken to measure anti-CarLA IgA antibody (CarLA-IgA) levels in the saliva of 5-12 month old farmed red and wapiti-cross-red deer (wapx) grazed together and to attempt to relate these levels to parasite burdens and productivity. The study showed that salivary CarLA-IgA levels peaked in June (late autumn) and October (mid spring), but the levels in wapx deer were significantly lower than in red deer. Over the May-December period 55% of red deer had CarLA-IgA values ≥2 units compared with 26% of wapx deer and over this period red deer had consistently lower adult abomasal parasite burdens than wapx deer. The average number of adult abomasal nematodes was significantly lower at each slaughter from May to December for all deer with CarLA-IgA ≥2 units vs <2 units. There were no demonstrable correlations with liveweight gain in these small groups of deer.