SAE Level 3 Autonomous Driving Technology of the ETRI

Min, Kyoungwook; Han, Seung‐Jun; Choi, Dooho; Sung, Kyungbok; Choi, Jeongdan

doi:10.1109/ictc46691.2019.8939765

Cited by 26 publications

(9 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Tomando el CG como punto de referencia para realizar la sumatoria de torques, se puede mostrar que el modelo que representa la dinámica de la plataforma está dado por las ecuaciones (4) y (5).…”

Section: Figura 3 Marco De Referencia Localunclassified

“…Uno de los problemas que conlleva trabajar en un entorno externo, es estimar la posición actual del vehículo, como es estudiado en Wang y Lambert [3]. Así mismo, para lograr una percepción más completa del entorno, se suelen utilizar múltiples sensores, cámaras y/o unidades inerciales que llevan al robot a poseer un alto grado de procesamiento para la toma de decisiones durante la ruta, que va desde un adecuado seguimiento a una trayectoria hasta poseer alta capacidad de maniobrabilidad en su entorno, tal como se puede evidenciar en Reke et al [4], permitiendo el desarrollo de nuevos robots, con gran capacidad de autonomía, como es el resultado de Min et al [5], donde hacen uso de un auto eléctrico comercial añadiendo cámaras, láser 3D, sistemas de localización y toda una arquitectura de software que le permite reconocer personas y señales de tránsito logrando una navegación segura en un entorno virtual.…”

Section: Introductionunclassified

See 1 more Smart Citation

Conducción segura de una plataforma móvil en configuración diferencial, basado en técnica de control predictivo.

Gonzalez

Guzmán²,

Guevara³

2021

Ing.USBMed

View full text Add to dashboard Cite

Dentro del proceso de conducción segura, particularmente en el campo del agro donde interviene una plataforma robótica, se hace necesario salvaguardar tanto la estructura como su entorno. En la literatura se pueden encuentran aplicaciones orientadas a ‘’self-driving’’, donde el objetivo es conseguir estrategias que guíen y controlen sistemas como automóviles o robots inmersos en ambientes dinámicos. El presente trabajo plantea una opción para que un robot en configuración diferencial (específicamente la plataforma CERES) se desplace de manera segura en un cultivo, previniendo colisiones o daños a ella y su entorno. De esta manera, se tiene como resultado la implementación de un control basado en una técnica de control predictivo, donde se integran las restricciones dinámicas y las saturaciones de las señales de control dentro de la función objetivo a solucionar. El control desarrollado considerar las restricciones de velocidad máximas y mínimas proporcionadas por los motores eléctricos utilizados. El error cuadrático medio y su varianza fueron los criterios estadísticos usados como métricas a la hora de verificar el comportamiento del sistema al seguir una trayectoria referencia.

show abstract

Section: Figura 3 Marco De Referencia Localunclassified

Section: Introductionunclassified

Conducción segura de una plataforma móvil en configuración diferencial, basado en técnica de control predictivo.

Gonzalez

Guzmán²,

Guevara³

2021

Ing.USBMed

View full text Add to dashboard Cite

show abstract

“…The perception functionality could be susceptible to corruption or misjudgment in different road situations, weather conditions, and traffic environments. Therefore, ensuring the safe and reliable operation of the autonomous perception system in various driving scenarios is of utmost importance as it will directly impact the reliability and safety performance of the self-driving systems [ 1 ]. The test coverage impacts the quality of testing for autonomous driving systems.…”

Section: Introductionmentioning

confidence: 99%

Development of an Effective Corruption-Related Scenario-Based Testing Approach for Robustness Verification and Enhancement of Perception Systems in Autonomous Driving

Hsiang,

Chen

2024

Sensors

View full text Add to dashboard Cite

Given that sensor-based perception systems are utilized in autonomous vehicle applications, it is essential to validate such systems to ensure their robustness before they are deployed. In this study, we propose a comprehensive simulation-based process to verify and enhance the robustness of sensor-based perception systems in relation to corruption. Firstly, we introduce a methodology and scenario-based corruption generation tool for creating a variety of simulated test scenarios. These scenarios can effectively mimic real-world traffic environments, with a focus on corruption types that are related to safety concerns. An effective corruption similarity filtering algorithm is then proposed to eliminate corruption types with high similarity and identify representative corruption types that encompass all considered corruption types. As a result, we can create efficient test scenarios for corruption-related robustness with reduced testing time and comprehensive scenario coverage. Subsequently, we conduct vulnerability analysis on object detection models to identify weaknesses and create an effective training dataset for enhancing model vulnerability. This improves the object detection models’ tolerance to weather and noise-related corruptions, ultimately enhancing the robustness of the perception system. We use case studies to demonstrate the feasibility and effectiveness of the proposed procedures for verifying and enhancing robustness. Furthermore, we investigate the impact of various “similarity overlap threshold” parameter settings on scenario coverage, effectiveness, scenario complexity (size of training and testing datasets), and time costs.

show abstract

“…EVs become the crucial component of VECNs. With the popularization of high level intelligent assisted driving and other applications [16], the computation resource capacity of EVs is improved greatly [17]. Specially, the idle computation resources of the moving vehicles [18], the vehicle platoon [19], and the vehicles at roadside parking lots [20] can be integrated to support the computing needs in VECNs.…”

Section: Introductionmentioning

confidence: 99%

Smart and Efficient EV Charging Navigation Scheme in Vehicular Edge Computing Networks

Li¹,

Chen²,

Liu³

et al. 2023

Preprint

View full text Add to dashboard Cite

With the increasing number of electric fast charging stations (FCSs) deployed along roadsides of both urban roads and highways, the long-distance travel of electric vehicles (EVs) becomes possible. The EV charging navigation scheme is significant for the quality of user experience. However, the variable conditions of both power grid and traffic networks make it a serious challenge. In this paper, we propose an efficient EV charging navigation scheme while considering both the electric and computation resource sharing. With the support of vehicular edge computing networks in intelligent transportation systems (ITSs), EVs perform both the flexible power load and edge computing nodes. When the traffic network in the established route starts to become congested, EVs can select to enter the nearest FCS. In addition to being supplemented by electric resources, EVs also benefit by sharing their own computing resource with FCSs. We formulate the EV charging navigation as a mixed integer programming problem, the EV moving route planning, FCS selection, and staying time in FCSs are optimized, to balance the relationships among the traveling time, traveling cost and reward. To address the influence caused by the randomness of traffic conditions and charging prices, a two-stage charging navigation algorithm combined with A* algorithm and deep reinforcement learning (DRL) is proposed, with a novel designed reward function. Eventually, numerous experimental results show the effectiveness of the proposed schemes.

show abstract

SAE Level 3 Autonomous Driving Technology of the ETRI

Cited by 26 publications

References 3 publications

Conducción segura de una plataforma móvil en configuración diferencial, basado en técnica de control predictivo.

Conducción segura de una plataforma móvil en configuración diferencial, basado en técnica de control predictivo.

Development of an Effective Corruption-Related Scenario-Based Testing Approach for Robustness Verification and Enhancement of Perception Systems in Autonomous Driving

Smart and Efficient EV Charging Navigation Scheme in Vehicular Edge Computing Networks

Contact Info

Product

Resources

About