scholarly journals Sequential lidar sensor system simulation: a modular approach for simulation-based safety validation of automated driving

2020 ◽  
Vol 5 (3-4) ◽  
pp. 187-197
Author(s):  
Philipp Rosenberger ◽  
Martin Friedrich Holder ◽  
Nicodemo Cianciaruso ◽  
Philip Aust ◽  
Jonas Franz Tamm-Morschel ◽  
...  
Keyword(s):  
Simulation Models ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Modular Approach ◽  
Active Perception ◽  
Automated Driving ◽  
Test Procedures ◽  
Sequential Approach ◽  
Simulation Based ◽  
Tracking Model

Abstract Validating safety is an unsolved challenge before autonomous driving on public roads is possible. Since only the use of simulation-based test procedures can lead to an economically viable solution for safety validation, computationally efficient simulation models with validated fidelity are demanded. A central part of the overall simulation tool chain is the simulation of the perception components. In this work, a sequential modular approach for simulation of active perception sensor systems is presented on the example of lidar. It enables the required level of fidelity of synthetic object list data for safety validation using beforehand simulated point clouds. The elaborated framework around the sequential modules provides standardized interfaces packaging for co-simulation such as Open Simulation Interface (OSI) and Functional Mockup Interface (FMI), while providing a new level of modularity, testability, interchangeability, and distributability. The fidelity of the sequential approach is demonstrated on an everyday scenario at an intersection that is performed in reality at first and reproduced in simulation afterwards. The synthetic point cloud is generated by a sensor model with high fidelity and processed by a tracking model afterwards, which, therefore, outputs bounding boxes and trajectories that are close to reality.

A simulation-based case study for powertrain efficiency improvement by automated driving functions

2018 ◽  
Vol 233 (5) ◽  
pp. 1320-1330 ◽  
Author(s):  
Thorsten Plum ◽  
Marius Wegener ◽  
Markus Eisenbarth ◽  
Ziqi Ye ◽  
Konstantin Etzold ◽  
...  
Keyword(s):  
Energy Demand ◽  
Simulation Models ◽  
Pollutant Emissions ◽  
Traffic Light ◽  
Automated Driving ◽  
Virtual Testing ◽  
Vehicle Simulation ◽  
Simulation Based ◽  
Testing Environments

An increasing level of driving automation and a successive electrification of modern powertrains enable a higher degree of freedom to improve vehicle fuel efficiency and reduce pollutant emissions. Currently, both domains themselves, driving automation as well as powertrain electrification, face the challenge of a rising development complexity with extensive use of virtual testing environments. However, state-of-the-art virtual testing environments typically strictly focus on just one domain and neglect the other. This paper shows the results of a simulation-based case study considering both domains simultaneously. The influence of energy saving automated functionalities on a conventional, a hybrid, and a pure electric powertrain is investigated for a carefully selected inner-city driving scenario. The vehicle simulation models for the different powertrain configurations are calibrated using test bench results and vehicle measurements. A model predictive acceleration controller is developed for realizing the speed optimization function. By considering traffic conditions such as traffic light schedules and a preceding vehicle as the boundary conditions, unnecessary accelerations and decelerations are avoided to reduce the energy demand. The case study is realized by applying this function to the three powertrains variants. As a final result, a clear difference in energy demand is observed: the hybrid powertrain benefits the most in terms of energy demand reduction in the given use case. The results clearly underscore that in future vehicle development programs, the powertrain and the real-world driving functionalities have to be optimized simultaneously to minimize the energy demand during everyday vehicle operation.

scholarly journals Change in Microscopic Traffic Simulation Practice with Respect to the Emerging Automated Driving Technology

2021 ◽  
Author(s):  
Xuan Fang ◽  
Tamás Tettamanti
Keyword(s):  
Autonomous Vehicles ◽  
Traffic Simulation ◽  
Simulation Models ◽  
Autonomous Driving ◽  
Road Transport ◽  
Transport Sector ◽  
Microscopic Simulation ◽  
Automated Driving ◽  
Microscopic Traffic Simulation ◽  
Traffic Demand

It is believed that autonomous vehicles will replace conventional human drive vehicles in the next decades due to the emerging autonomous driving technology, which will definitely bring a massive transformation in the road transport sector. Due to the high complexity of traffic systems, efficient traffic simulation models for the assessment of this disruptive change are critical. The objective of this paper is to justify that the common practice of microscopic traffic simulation needs thorough revision and modification when it is applied with the presence of autonomous vehicles in order to get realistic results. Two high-fidelity traffic simulators (SUMO and VISSIM) were applied to show the sensitivity of microscopic simulation to automated vehicle’s behavior. Two traffic evaluation indicators (average travel time and average speed) were selected to quantitatively evaluate the macro-traffic performance of changes in driving behavior parameters (gap acceptance) caused by emerging autonomous driving technologies under different traffic demand conditions.

Assessing Fuel Economy From Automated Driving: Influence of Preview and Velocity Constraints

2016 ◽  
Author(s):  
Niket Prakash ◽  
Gionata Cimini ◽  
Anna G. Stefanopoulou ◽  
Matthew J. Brusstar
Keyword(s):  
Fuel Economy ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Automated Driving ◽  
Test Procedures ◽  
Optimal Velocity ◽  
Prediction Horizon ◽  
Allowable Error ◽  
Velocity Constraints ◽  
Lead Vehicle

Constrained optimization control techniques with preview are designed in this paper to derive optimal velocity trajectories in longitudinal vehicle following mode, while ensuring that the gap from the lead vehicle is both safe and short enough to prevent cut-ins from other lanes. The lead vehicle associated with the Federal Test Procedures (FTP) [1] is used as an example of the achieved benefits with such controlled velocity trajectories of the following vehicle. Fuel Consumption (FC) is indirectly minimized by minimizing the accelerations and decelerations as the autonomous vehicle follows the hypothetical lead. Implementing the cost function in offline Dynamic Programming (DP) with full drive cycle preview showed up to a 17% increase in Fuel Economy (FE). Real time implementation with Model Predictive Control (MPC) showed improvements in FE, proportional to the prediction horizon. Specifically, 20s preview MPC was able to match the DP results. A minimum of 1.5s preview of the lead vehicle velocity with velocity tracking of the lead was required to obtain an increase in FE. The optimal velocity trajectory found from these algorithms exceeded the presently allowable error from standard drive cycles for FC testing. However, the trajectory was still safe and acceptable from the perspective of traffic flow. Based on our results, regulators need to consider relaxing the constant velocity error margins around the standard velocity trajectories dictated by the FTP to encourage FE increase in autonomous driving.

scholarly journals Cloth simulation-based construction of pit-free canopy height models from airborne LiDAR data

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Wuming Zhang ◽  
Shangshu Cai ◽  
Xinlian Liang ◽  
Jie Shao ◽  
Ronghai Hu ◽  
...  
Keyword(s):  
Tree Height ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Lidar Data ◽  
Cloth Simulation ◽  
Simulation Based ◽  
Maximum Tree Height ◽  
Airborne Lidar Data ◽  
Better Than

Abstract Background The universal occurrence of randomly distributed dark holes (i.e., data pits appearing within the tree crown) in LiDAR-derived canopy height models (CHMs) negatively affects the accuracy of extracted forest inventory parameters. Methods We develop an algorithm based on cloth simulation for constructing a pit-free CHM. Results The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details. Our pit-free CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms, as evidenced by the lowest average root mean square error (0.4981 m) between the reference CHMs and the constructed pit-free CHMs. Moreover, our pit-free CHMs show the best performance overall in terms of maximum tree height estimation (average bias = 0.9674 m). Conclusion The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.

scholarly journals Parallel Point Clouds: Hybrid Point Cloud Generation and 3D Model Enhancement via Virtual–Real Integration

2021 ◽  
Vol 13 (15) ◽  
pp. 2868
Author(s):  
Yonglin Tian ◽  
Xiao Wang ◽  
Yu Shen ◽  
Zhongzheng Guo ◽  
Zilei Wang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Closed Loop ◽  
Real Data ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Training Model ◽  
Labor Cost ◽  
Open Loop ◽  
Training Dataset ◽  
Data Annotation

Three-dimensional information perception from point clouds is of vital importance for improving the ability of machines to understand the world, especially for autonomous driving and unmanned aerial vehicles. Data annotation for point clouds is one of the most challenging and costly tasks. In this paper, we propose a closed-loop and virtual–real interactive point cloud generation and model-upgrading framework called Parallel Point Clouds (PPCs). To our best knowledge, this is the first time that the training model has been changed from an open-loop to a closed-loop mechanism. The feedback from the evaluation results is used to update the training dataset, benefiting from the flexibility of artificial scenes. Under the framework, a point-based LiDAR simulation model is proposed, which greatly simplifies the scanning operation. Besides, a group-based placing method is put forward to integrate hybrid point clouds, via locating candidate positions for virtual objects in real scenes. Taking advantage of the CAD models and mobile LiDAR devices, two hybrid point cloud datasets, i.e., ShapeKITTI and MobilePointClouds, are built for 3D detection tasks. With almost zero labor cost on data annotation for newly added objects, the models (PointPillars) trained with ShapeKITTI and MobilePointClouds achieved 78.6% and 60.0% of the average precision of the model trained with real data on 3D detection, respectively.

Independent wheel control system design for highly automated driving

2021 ◽  
pp. 095440702110065
Author(s):  
Shihuan Li ◽  
Lei Wang
Keyword(s):  
Control System ◽  
Control Method ◽  
Autonomous Driving ◽  
Control System Design ◽  
Steering Control ◽  
Automated Driving ◽  
Accuracy And Precision ◽  
Actual Observation ◽  
Control Scheme ◽  
Interference Measurement

For L4 and above autonomous driving levels, the automatic control system has been redundantly designed, and a new steering control method based on brake has been proposed; a new dual-track model has been established through multiple driving tests. The axle part of the model was improved, the accuracy of the transfer function of the model was verified again through acceleration-slide tests; a controller based on interference measurement was designed on the basis of the model, and the relationships between the controller parameters was discussed. Through the linearization of the controller, the robustness of uncertain automobile parameters is discussed; the control scheme is tested and verified through group driving test, and the results prove that the accuracy and precision of the controller meet the requirements, the robustness stability is good. Moreover, the predicted value of the model fits well with the actual observation value, the proposal of this method provides a new idea for avoiding car out of control.

scholarly journals Strong-Weak Feature Alignment for 3D Object Detection

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1205
Author(s):  
Zhiyu Wang ◽  
Li Wang ◽  
Bin Dai
Keyword(s):  
Object Detection ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Feature Representation ◽  
Alignment Algorithm ◽  
3D Object ◽  
3D Point Clouds ◽  
Object Feature ◽  
3D Object Detection ◽  
Feature Alignment

Object detection in 3D point clouds is still a challenging task in autonomous driving. Due to the inherent occlusion and density changes of the point cloud, the data distribution of the same object will change dramatically. Especially, the incomplete data with sparsity or occlusion can not represent the complete characteristics of the object. In this paper, we proposed a novel strong–weak feature alignment algorithm between complete and incomplete objects for 3D object detection, which explores the correlations within the data. It is an end-to-end adaptive network that does not require additional data and can be easily applied to other object detection networks. Through a complete object feature extractor, we achieve a robust feature representation of the object. It serves as a guarding feature to help the incomplete object feature generator to generate effective features. The strong–weak feature alignment algorithm reduces the gap between different states of the same object and enhances the ability to represent the incomplete object. The proposed adaptation framework is validated on the KITTI object benchmark and gets about 6% improvement in detection average precision on 3D moderate difficulty compared to the basic model. The results show that our adaptation method improves the detection performance of incomplete 3D objects.

scholarly journals Investigations on Temperature Fields during Laser Beam Melting by Means of Process Monitoring and Multiscale Process Modelling

2014 ◽  
Vol 6 ◽  
pp. 217584 ◽  
Author(s):  
J. Schilp ◽  
C. Seidel ◽  
H. Krauss ◽  
J. Weirather
Keyword(s):  
Process Monitoring ◽  
Manufacturing Process ◽  
Computation Time ◽  
Simulation Models ◽  
Process Models ◽  
Temperature Fields ◽  
Transient Temperature ◽  
Transient Temperature Field ◽  
Laser Beam Melting ◽  
Simulation Based

Process monitoring and modelling can contribute to fostering the industrial relevance of additive manufacturing. Process related temperature gradients and thermal inhomogeneities cause residual stresses, and distortions and influence the microstructure. Variations in wall thickness can cause heat accumulations. These occur predominantly in filigree part areas and can be detected by utilizing off-axis thermographic monitoring during the manufacturing process. In addition, numerical simulation models on the scale of whole parts can enable an analysis of temperature fields upstream to the build process. In a microscale domain, modelling of several exposed single hatches allows temperature investigations at a high spatial and temporal resolution. Within this paper, FEM-based micro- and macroscale modelling approaches as well as an experimental setup for thermographic monitoring are introduced. By discussing and comparing experimental data with simulation results in terms of temperature distributions both the potential of numerical approaches and the complexity of determining suitable computation time efficient process models are demonstrated. This paper contributes to the vision of adjusting the transient temperature field during manufacturing in order to improve the resulting part's quality by simulation based process design upstream to the build process and the inline process monitoring.

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