Dataset Information

An Intelligent Self-Driving Truck System for Highway Transportation.

ABSTRACT: Recently, there have been many advances in autonomous driving society, attracting a lot of attention from academia and industry. However, existing studies mainly focus on cars, extra development is still required for self-driving truck algorithms and models. In this article, we introduce an intelligent self-driving truck system. Our presented system consists of three main components, 1) a realistic traffic simulation module for generating realistic traffic flow in testing scenarios, 2) a high-fidelity truck model which is designed and evaluated for mimicking real truck response in real world deployment, and 3) an intelligent planning module with learning-based decision making algorithm and multi-mode trajectory planner, taking into account the truck's constraints, road slope changes, and the surrounding traffic flow. We provide quantitative evaluations for each component individually to demonstrate the fidelity and performance of each part. We also deploy our proposed system on a real truck and conduct real world experiments which show our system's capacity of mitigating sim-to-real gap. Our code is available at https://github.com/InceptioResearch/IITS.

SUBMITTER: Wang D

PROVIDER: S-EPMC9137412 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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An Intelligent Self-Driving Truck System for Highway Transportation.

Wang Dawei D Gao Lingping L Lan Ziquan Z Li Wei W Ren Jiaping J Zhang Jiahui J Zhang Peng P Zhou Pei P Wang Shengao S Pan Jia J Manocha Dinesh D Yang Ruigang R

Frontiers in neurorobotics 20220513

Recently, there have been many advances in autonomous driving society, attracting a lot of attention from academia and industry. However, existing studies mainly focus on cars, extra development is still required for self-driving truck algorithms and models. In this article, we introduce an intelligent self-driving truck system. Our presented system consists of three main components, 1) a realistic traffic simulation module for generating realistic traffic flow in testing scenarios, 2) a high-fi ...[more]

PMID: 35645759

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Project description:While emerging technology for self-driving automation in vehicles progresses rapidly, the transition to an era of roads full of fully connected and automated vehicles (CAVs) may take longer than expected. Until then, it is inevitable that CAVs should coexist and interact with drivers of non-autonomous vehicles (NAVs) in urban roads. During this period of transition, it is critical to provide road safety with the mixed vehicular traffic and uncertainty caused by human drivers. To investigate the issues caused by the coexistence and interaction with humans, we propose to build a component-based and interactive intelligent transportation cyber-physical systems (ITCPS) framework. Our design of the interactive ITCPS framework aims to provide a standardized structure for users by defining core components. The framework is specified by behavior models and interfaces for the desired ITCPS components and is implemented as a form of human and hardware-in-the-loop system. We developed an intersection crossing assistance service and an automatic emergency braking service as an example of practical applications using the framework. To evaluate the framework, we tested its performance to show how effectively it operates while supporting real-time processing. The results indicate that it satisfies the timing requirements of vehicle-to-vehicle (V2V) communication and the limited processing time required for performing the functions of behavior models, even though the traffic volume reaches the road capacity. A case study using statistical analysis is conducted to assess the practical value of the developed experimental environment. The results of the case study validate the reliability among the specified variables for the experiments involving human drivers. It has shown that V2V communication support has positive effects on road safety, including intersection safety, braking events, and perception-reaction time (PRT) of the drivers. Furthermore, V2V communication support and PRT are identified as the important indicators affecting road safety at an un-signalized intersection. The proposed interactive framework is expected to contribute in constructing a comprehensive environment for the urban ITCPS and providing experimental support for the analysis of human behavior in the coexistence environment.

Dataset Information

An Intelligent Self-Driving Truck System for Highway Transportation.

Publications

An Intelligent Self-Driving Truck System for Highway Transportation.

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