scholarly journals Modules and Techniques for Motion Planning: An Industrial Perspective

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 420
Author(s):  
Stefano Quer ◽  
Luz Garcia
Keyword(s):  
Data Fusion ◽  
Autonomous Driving ◽  
Random Trees ◽  
Approximation Techniques ◽  
Main Research ◽  
Autonomous Cars ◽  
Critical Issues ◽  
Relative Errors ◽  
Path Planner ◽  
Many Core

Research on autonomous cars has become one of the main research paths in the automotive industry, with many critical issues that remain to be explored while considering the overall methodology and its practical applicability. In this paper, we present an industrial experience in which we build a complete autonomous driving system, from the sensor units to the car control equipment, and we describe its adoption and testing phase on the field. We report how we organize data fusion and map manipulation to represent the required reality. We focus on the communication and synchronization issues between the data-fusion device and the path-planner, between the CPU and the GPU units, and among different CUDA kernels implementing the core local planner module. In these frameworks, we propose simple representation strategies and approximation techniques which guarantee almost no penalty in terms of accuracy and large savings in terms of memory occupation and memory transfer times. We show how we adopt a recent implementation on parallel many-core devices, such as CUDA-based GPGPU, to reduce the computational burden of rapidly exploring random trees to explore the state space along with a given reference path. We report on our use of the controller and the vehicle simulator. We run experiments on several real scenarios, and we report the paths generated with the different settings, with their relative errors and computation times. We prove that our approach can generate reasonable paths on a multitude of standard maneuvers in real time.

Autonomy and Responsibility in Hybrid Systems

2017 ◽  
Author(s):  
Wulf Loh ◽  
Janina Loh
Keyword(s):  
Hybrid Systems ◽  
Hybrid System ◽  
Moral Dilemma ◽  
Autonomous Driving ◽  
Shared Responsibility ◽  
Driving System ◽  
Autonomous Cars ◽  
Self Driving Cars ◽  
Trolley Cases ◽  
Autonomous Driving System

In this chapter, we give a brief overview of the traditional notion of responsibility and introduce a concept of distributed responsibility within a responsibility network of engineers, driver, and autonomous driving system. In order to evaluate this concept, we explore the notion of man–machine hybrid systems with regard to self-driving cars and conclude that the unit comprising the car and the operator/driver consists of such a hybrid system that can assume a shared responsibility different from the responsibility of other actors in the responsibility network. Discussing certain moral dilemma situations that are structured much like trolley cases, we deduce that as long as there is something like a driver in autonomous cars as part of the hybrid system, she will have to bear the responsibility for making the morally relevant decisions that are not covered by traffic rules.

Autonomous Driving Scenario Generation in Overtake Manoeuvres Through Data Fusion

2020 ◽  
pp. 786-794
Author(s):  
Henrique de Carvalho Pinheiro ◽  
Diego Cruz Stanke ◽  
Alessandro Ferraris ◽  
Massimiliana Carello ◽  
Giovanni Gabiati ◽  
...  
Keyword(s):  
Data Fusion ◽  
Autonomous Driving ◽  
Scenario Generation ◽  
Driving Scenario

scholarly journals Real - Time Trajectory and Velocity Planning for Autonomous Vehicles

2021 ◽  
Vol 10 (5) ◽  
pp. 439-448
Author(s):  
Hrishikesh Dey ◽  
Rithika Ranadive ◽  
Abhishek Chaudhari
Keyword(s):  
Path Planning ◽  
Velocity Profile ◽  
Real Time ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Autonomous Driving ◽  
Navigation Algorithm ◽  
Velocity Planning ◽  
Planning Algorithm ◽  
Path Planner

Path planning algorithm integrated with a velocity profile generation-based navigation system is one of the most important aspects of an autonomous driving system. In this paper, a real-time path planning solution to obtain a feasible and collision-free trajectory is proposed for navigating an autonomous car on a virtual highway. This is achieved by designing the navigation algorithm to incorporate a path planner for finding the optimal path, and a velocity planning algorithm for ensuring a safe and comfortable motion along the obtained path. The navigation algorithm was validated on the Unity 3D Highway-Simulated Environment for practical driving while maintaining velocity and acceleration constraints. The autonomous vehicle drives at the maximum specified velocity until interrupted by vehicular traffic, whereas then, the path planner, based on the various constraints provided by the simulator using µWebSockets, decides to either decelerate the vehicle or shift to a more secure lane. Subsequently, a splinebased trajectory generation for this path results in continuous and smooth trajectories. The velocity planner employs an analytical method based on trapezoidal velocity profile to generate velocities for the vehicle traveling along the precomputed path. To provide smooth control, an s-like trapezoidal profile is considered that uses a cubic spline for generating velocities for the ramp-up and ramp-down portions of the curve. The acceleration and velocity constraints, which are derived from road limitations and physical systems, are explicitly considered. Depending upon these constraints and higher module requirements (e.g., maintaining velocity, and stopping), an appropriate segment of the velocity profile is deployed. The motion profiles for all the use-cases are generated and verified graphically.

scholarly journals The Mechanisation and Automation of the IBS Construction Approach in Malaysia

2018 ◽  
Vol 3 (10) ◽  
pp. 167-177 ◽  
Author(s):  
Siti Syariazulfa Kamaruddin ◽  
Mohammad Fadhil Mohammad ◽  
Rohana Mahbub ◽  
Khairani Ahmad
Keyword(s):  
Open Access ◽  
Publishing House ◽  
Modern Method ◽  
Structured Interviews ◽  
Main Research ◽  
Current State ◽  
Critical Issues ◽  
International Publishing ◽  
Building System ◽  
Open Access Article

The objective of the main research is to investigate the current state of implementation of mechanisation and automation in the Industrialised Building System (IBS). The aim of this paper however, is to provide an overview on the implementation of mechanisation and automation in the IBS construction approach. Questionnaire survey and semi structured interviews were conducted to collect the viewpoints among contractors and manufacturers of IBS to ensure the richness of the data collected. The results illustrated that although most respondents have agreed on most critical issues in the implementation of mechanisation and automation, they were not ready to move forward. These responses were quite consistent across the professions. Keywords: Modern method of construction (MMC); industrialised building system (IBS); mechanisation and automation; environment. eISSN 2514-751X © 2018. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open-access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/aje-bs.v3i10.324

scholarly journals A Smart Many-Core Implementation of a Motion Planning Framework along a Reference Path for Autonomous Cars

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 177 ◽  
Author(s):  
Gianpiero Cabodi ◽  
Paolo Camurati ◽  
Alessandro Garbo ◽  
Michele Giorelli ◽  
Stefano Quer ◽  
...  
Keyword(s):  
Real Time ◽  
Graphics Processing Units ◽  
Data Communication ◽  
Computational Effort ◽  
Random Trees ◽  
Sequential Algorithm ◽  
Original Algorithm ◽  
Reference Path ◽  
Autonomous Cars

Research on autonomous cars, early intensified in the 1990s, is becoming one of the main research paths in automotive industry. Recent works use Rapidly-exploring Random Trees to explore the state space along a given reference path, and to compute the minimum time collision-free path in real time. Those methods do not require good approximations of the reference path, they are able to cope with discontinuous routes, they are capable of navigating in realistic traffic scenarios, and they derive their power from an extensive computational effort directed to improve the quality of the trajectory from step to step. In this paper, we focus on re-engineering an existing state-of-the-art sequential algorithm to obtain a CUDA-based GPGPU (General Purpose Graphics Processing Units) implementation. To do that, we show how to partition the original algorithm among several working threads running on the GPU, how to propagate information among threads, and how to synchronize those threads. We also give detailed evidence on how to organize memory transfers between the CPU and the GPU (and among different CUDA kernels) such that planning times are optimized and the available memory is not exceeded while storing massive amounts of fuse data. To sum up, in our application the GPU is used for all main operations, the entire application is developed in the CUDA language, and specific attention is paid to concurrency, synchronization, and data communication. We run experiments on several real scenarios, comparing the GPU implementation with the CPU one in terms of the quality of the generated paths and in terms of computation (wall-clock) times. The results of our experiments show that embedded GPUs can be used as an enabler for real-time applications of computationally expensive planning approaches.

scholarly journals Simultaneous Localization and Map Change Update for the High Definition Map-Based Autonomous Driving Car

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3145 ◽  
Author(s):  
Kichun Jo ◽  
Chansoo Kim ◽  
Myoungho Sunwoo
Keyword(s):  
Evidence Theory ◽  
Autonomous Driving ◽  
High Definition ◽  
Driving System ◽  
The Real ◽  
Traffic Conditions ◽  
Autonomous Cars ◽  
Vehicle Position ◽  
Autonomous Driving System ◽  
Real Traffic

High Definition (HD) maps are becoming key elements of the autonomous driving because they can provide information about the surrounding environment of the autonomous car without being affected by the real-time perception limit. To provide the most recent environmental information to the autonomous driving system, the HD map must maintain up-to-date data by updating changes in the real world. This paper presents a simultaneous localization and map change update (SLAMCU) algorithm to detect and update the HD map changes. A Dempster–Shafer evidence theory is applied to infer the HD map changes based on the evaluation of the HD map feature existence. A Rao–Blackwellized particle filter (RBPF) approach is used to concurrently estimate the vehicle position and update the new map state. The detected and updated map changes by the SLAMCU are reported to the HD map database in order to reflect the changes to the HD map and share the changing information with the other autonomous cars. The SLAMCU was evaluated through experiments using the HD map of traffic signs in the real traffic conditions.

scholarly journals Baseline-Free Adaptive Crack Localization for Operating Stepped Rotors Based on Multiscale Data Fusion

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5693
Author(s):  
Zhiwen Lu ◽  
Shancheng Cao ◽  
Rui Yuan ◽  
Yong Lv
Keyword(s):  
Data Fusion ◽  
Reference Model ◽  
Damage Index ◽  
Energy Operator ◽  
Noise Robustness ◽  
Experimental Investigations ◽  
Multiple Cracks ◽  
Practical Applications ◽  
Critical Issues ◽  
Baseline Information

Crack localization in running rotors is very important and full of challenges for machinery operation and maintenance. Characteristic deflection shapes or their derivatives based methods seem to be promising for crack localization in rotors. Despite the substantial advantages, several critical issues still need to be addressed to enhance the efficiency of this kind of method for practical applications. Two problems are considered in this work: 1. How to localize single or multiple cracks accurately avoiding the interference of commonly existing steps without baseline information on pristine rotors; 2. How to improve the crack localization performance under a noisy environment. To circumvent the issues, a novel baseline-free adaptive crack localization method is proposed based on data fusion of multiscale super-harmonic characteristic deflection shapes (SCDSs). In this method, crack induced asymmetry and nonlinearity of crack breathing are utilized to simultaneously eliminate the interference from the steps without a reference model. To enhance the noise robustness, the multiscale representations of SCDSs are made in Gaussian multiscale space, and Teager energy operator is applied to the multiscale SCDSs to amplify the crack induced singularities and construct the multiscale Teager super-harmonic characteristic deflection shapes (TSCDSs). Moreover, fractal dimension is designed as an evaluator to select the proper multiscale TSCDSs for data fusion adaptively. Then, a new damage index is derived for crack localization by Dempster-Shafer’s (D-S) evidence fusion of the adaptively selected multiscale TSCDSs. Finally, the feasibility and the effectiveness are verified by both numerical and experimental investigations.

scholarly journals Advances in verification of ReLU neural networks

2020 ◽  
Author(s):  
Ansgar Rössig ◽  
Milena Petkovic
Keyword(s):  
Neural Networks ◽  
Information Processing ◽  
Computational Study ◽  
Autonomous Driving ◽  
Mixed Integer ◽  
Data Set ◽  
Approximation Techniques ◽  
Neural Information ◽  
Neural Information Processing ◽  
Algorithmic Approaches

Abstract We consider the problem of verifying linear properties of neural networks. Despite their success in many classification and prediction tasks, neural networks may return unexpected results for certain inputs. This is highly problematic with respect to the application of neural networks for safety-critical tasks, e.g. in autonomous driving. We provide an overview of algorithmic approaches that aim to provide formal guarantees on the behaviour of neural networks. Moreover, we present new theoretical results with respect to the approximation of ReLU neural networks. On the other hand, we implement a solver for verification of ReLU neural networks which combines mixed integer programming with specialized branching and approximation techniques. To evaluate its performance, we conduct an extensive computational study. For that we use test instances based on the ACAS Xu system and the MNIST handwritten digit data set. The results indicate that our approach is very competitive with others, i.e. it outperforms the solvers of Bunel et al. (in: Bengio, Wallach, Larochelle, Grauman, Cesa-Bianchi, Garnett (eds) Advances in neural information processing systems (NIPS 2018), 2018) and Reluplex (Katz et al. in: Computer aided verification—29th international conference, CAV 2017, Heidelberg, Germany, July 24–28, 2017, Proceedings, 2017). In comparison to the solvers ReluVal (Wang et al. in: 27th USENIX security symposium (USENIX Security 18), USENIX Association, Baltimore, 2018a) and Neurify (Wang et al. in: 32nd Conference on neural information processing systems (NIPS), Montreal, 2018b), the number of necessary branchings is much smaller. Our solver is publicly available and able to solve the verification problem for instances which do not have independent bounds for each input neuron.

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