Fast Prototype Framework for Deep Reinforcement Learning-based Trajectory Planner

Reinforcement Learning, as one of the main approaches of machine learning, has been gaining high popularity in recent years, which also affects the vehicle industry and research focusing on automated driving. However, these techniques, due to their self-training approach, have high computational resource requirements. Their development can be separated into training with simulation, validation through vehicle dynamics software, and real-world tests. However, ensuring portability of the designed algorithms between these levels is difficult. A case study is also given to provide better insight into the development process, in which an online trajectory planner is trained and evaluated in both vehicle simulation and real-world environments

Framework for Data Acquisition and Fusion of Camera and Radar for Autonomous Vehicle Systems

The primary contribution is the development of the data collection testing methodology for autonomous driving systems of a hybrid electric passenger vehicle. As automotive manufacturers begin to develop adaptive cruise control technology in vehicles, progress is being made toward the development of fully-autonomous vehicles. Adaptive cruise control capability is classified into five levels defined by the Society of Automotive Engineering. Some vehicles under development have attained higher levels of autonomy, but the focus of most commercial development is Level 2 autonomy. As the level of autonomy increases, the sensor technology becomes more advanced with a sensor suite which includes radar, camera, and vehicle-to-everything radio. Sensors must detect the objects around the vehicle to be able for communicate the data to the adaptive cruise control algorithm. If a vehicle is in an accident, the driver is typically responsible for the damages, but with an autonomous vehicle, there might not be a driver. A process to guarantee a vehicle will perform as it was developed is critical to a vehicle’s development and testing. The goal of this work is to implement a verification and validation system that can be used on adaptive cruise control systems. The system developed in this paper used different testing environments such as model-in-the-loop, hardware-in-the-loop, and vehicle-in-the-loop, to fully validate an autonomous vehicle. A systematic data acquisition process has been developed to support autonomous vehicle development. The data that was taken had an organized way of comparing the results in each environment. Requirements management, vehicle logbook, and test case creation played a vital role in combining the information across the environments. The method produced a consumer-ready adaptive cruise control system in a 2019 Chevrolet Blazer RS. The vehicle was able to perform at an Advanced Vehicle Technology Competition where the adaptive cruise control system placed 1st in Connected and Automated Vehicle Perception System & Adaptive Cruise Control Drive Quality Evaluation. Results are presented that illustrate the utility of the data acquisition and multi-layer testing process for autonomous vehicle development

The Development of the Digital Twin Platform for Smart Mobility Systems With High-Resolution 3D Data

69A3551847102This project develops the main modules and algorithm models for the digital twin platform for a smart mobility testing ground currently under construction. LiDAR (Line Detection And Ranging)-sensor-based object detection and 3D infrastructure modeling modules are developed and tested in the project. The developed digital twin model is pilot tested to conduct near-miss analysis at the intersections of the DataCity Smart Mobility Testing Ground in New Brunswick, NJ

Sustainable Pavements and Road Materials

The SIIV Arena is a space for discussion in which PhD students and young scholars from various universities illustrate their research on topics of specific interest for the Scientific Disciplinary Sector ICAR/04 “Roads, Railways and Airports”. This volume collects the proceedings of the 7th SIIV Arena, held in Naples on 9 September 2022, and held as part of the XVIII International SIIV Summer School: "Sustainable Pavements and Road Materials". The use of construction and maintenance technologies based on principles of sustainability, resilience and circular economy, are a reference, in synergy with the use of secondary raw materials, for achieving adequate mechanical performance for the road structure, with a reduced environmental impact. and costs. The growth gradient recorded in the field of civil infrastructures has defined an incessant use of natural resources with consequent negative effects in terms of environmental sustainability. The reuse of waste in mix design processes and the use of in situ processing systems and/or "low energy" technologies (i.e., cold and/or warm asphalt mixtures) fully meet the objectives underlying the principles of the circular economy. This volume aims to collect the most innovative research in the sector presented in the context of the 7th SIIV Arena by analyzing aspects relating to the design, construction and maintenance of the pavement and road infrastructure as a whole.PublishedLa SIIV Arena è uno spazio di confronto nel quale dottorandi e giovani studiosi delle diverse sedi accademiche illustrano le loro ricerche su temi di specifico interesse per il Settore Scientifico Disciplinare ICAR/04 “Strade, Ferrovie e Aeroporti”. Questo volume raccoglie gli atti della 7a SIIV Arena, tenutasi a Napoli il 9 settembre 2022, e svoltasi nell’ambito della XVIII International SIIV Summer School: “Sustainable Pavements and Road Materials”. Il ricorso a tecnologie di costruzione e manutenzione basate su principi di sostenibilità, resilienza ed economia circolare, sono di riferimento, in sinergia con l’utilizzo di materie prime seconde, per il raggiungimento di adeguate performance meccaniche per il corpo stradale, a ridotto impatto ambientale e costi. Il gradiente di crescita registrato nell’ambito delle infrastrutture civili ha definito un incessante ricorso all’utilizzo di risorse naturali con conseguenti effetti negativi in termini di sostenibilità ambientale. Il reimpiego di rifiuti nei processi di mix design e il ricorso all’uso di sistemi di lavorazione in situ e/o tecnologie a “bassa energia” (i.e. conglomerati bituminosi a freddo e/o tiepidi) accolgono in pieno gli obiettivi alla base dei principi dell’economia circolare. Tale volume si propone di raccogliere le ricerche più innovative nel settore presentate nell’ambito della 7a SIIV Arena analizzando aspetti relativi alla progettazione, costruzione e manutenzione nel suo complesso delle pavimentazioni e corpo stradale

Sustainable Pavements and Road Materials : Proceedings of the 7th SIIV Arena, Naples, Italy 9th September 2022

[Italiano]: La SIIV Arena è uno spazio di confronto nel quale dottorandi e giovani studiosi delle diverse sedi accademiche illustrano le loro ricerche su temi di specifico interesse per il Settore Scientifico Disciplinare ICAR/04 “Strade, Ferrovie e Aeroporti”. Questo volume raccoglie gli atti della 7a SIIV Arena, tenutasi a Napoli il 9 settembre 2022, e svoltasi nell’ambito della XVIII International SIIV Summer School: “Sustainable Pavements and Road Materials”. Il ricorso a tecnologie di costruzione e manutenzione basate su principi di sostenibilità, resilienza ed economia circolare, sono di riferimento, in sinergia con l’utilizzo di materie prime seconde, per il raggiungimento di adeguate performance meccaniche per il corpo stradale, a ridotto impatto ambientale e costi. Il gradiente di crescita registrato nell’ambito delle infrastrutture civili ha definito un incessante ricorso all’utilizzo di risorse naturali con conseguenti effetti negativi in termini di sostenibilità ambientale. Il reimpiego di rifiuti nei processi di mix design e il ricorso all’uso di sistemi di lavorazione in situ e/o tecnologie a “bassa energia” (i.e. conglomerati bituminosi a freddo e/o tiepidi) accolgono in pieno gli obiettivi alla base dei principi dell’economia circolare. Tale volume si propone di raccogliere le ricerche più innovative nel settore presentate nell’ambito della 7a SIIV Arena analizzando aspetti relativi alla progettazione, costruzione e manutenzione nel suo complesso delle pavimentazioni e corpo stradale./[English]: The SIIV Arena is a space for discussion in which PhD students and young scholars from various universities illustrate their research on topics of specific interest for the Scientific Disciplinary Sector ICAR/04 “Roads, Railways and Airports”. This volume collects the proceedings of the 7th SIIV Arena, held in Naples on 9 September 2022, and held as part of the XVIII International SIIV Summer School: "Sustainable Pavements and Road Materials". The use of construction and maintenance technologies based on principles of sustainability, resilience and circular economy, are a reference, in synergy with the use of secondary raw materials, for achieving adequate mechanical performance for the road structure, with a reduced environmental impact. and costs. The growth gradient recorded in the field of civil infrastructures has defined an incessant use of natural resources with consequent negative effects in terms of environmental sustainability. The reuse of waste in mix design processes and the use of in situ processing systems and/or "low energy" technologies (i.e., cold and/or warm asphalt mixtures) fully meet the objectives underlying the principles of the circular economy. This volume aims to collect the most innovative research in the sector presented in the context of the 7th SIIV Arena by analyzing aspects relating to the design, construction and maintenance of the pavement and road infrastructure as a whole

Reaktion menschlicher (Mit-)Fahrer auf hochautomatisierte Fahrzeuge im Mischverkehr auf der Autobahn und im urbanen Raum

In the future, highly automated vehicles will be introduced in road traffic, first on highways, and later in urban areas. The introduction will result in a long transition phase with mixed traffic. This transition phase poses new challenges for humans as passengers inside highly automated vehicles and for humans as drivers interacting with these vehicles in mixed traffic. Thus far, human drivers lack experience with highly automated vehicles and driving in mixed traffic. In addition, it can be expected that highly automated vehicles will drive in a more rule-compliant and defensive way than human drivers. This may cause conflicts with human drivers in mixed traffic, and lead to passenger discomfort and perceived risk. This dissertation investigated how humans react to highly automated vehicles in mixed traffic, taking both inside perspective of passengers and the outside perspective of human drivers into account. To this end, four psychological experiments. From the outside perspective, this dissertation investigated human drivers’ first contact with highly automated vehicles in dyadic interactions on the highway (Study 1) and repeated contact on longer highway sections (Study 2). Results showed that human drivers rate the rule-compliant automated driving behavior as pleasant and safe in dyadic interactions. However, human drivers feel slowed down by preceding highly automated vehicles on longer stretches of highway, which can be a potential hazard. Furthermore, an external labelling of highly automated vehicles may be recommendable in the long run. From the inside perspective of passengers, this dissertation investigated urban mixed traffic interactions with cyclists and pedestrians in longitudinal traffic (Study 3) and at a junction (Study 4). Results show that passengers do not accept any risk during highly automated driving and passengers want an early behavioral reaction of the highly automated vehicle to vulnerable road users in the driving environment. Across the four studies, the present dissertation shows that highly automated vehicles drive noticeably differently, which both passengers and manual drivers can perceive. However, highly automated driving behavior is perceived as unpleasant at maximum, but not as dangerous. When designing highly automated driving functions, both driver and passenger preferences should be considered equally. Future studies should examine the preferences of human road users regarding automated driving behavior.In Zukunft werden hochautomatisierte Fahrzeuge im Straßenverkehr eingeführt, zunächst auf der Autobahn, und später auch im urbanen Raum. Die Einführung dieser Technologie resultiert in einer langen Übergangsphase mit Mischverkehr. Dieser Übergang stellt Menschen als Passagiere und Fahrer vor neue Herausforderungen. Bislang fehlt Fahrern die Erfahrung mit hochautomatisierten Fahrzeugen und dem Fahren im Mischverkehr. Zudem ist zu erwarten, dass sich hochautomatisierte Fahrzeuge regelkonformer und defensiver fahren als menschliche Fahrer. Das könnte zu Konflikten mit anderen Verkehrsteilnehmern, und zu Diskomfort und Risikoerleben beim Passagier führen. Diese Dissertation untersuchte mithilfe von psychologischen Experimenten wie menschliche Fahrer auf hochautomatisierte Systeme aus der Passagiersicht und aus der Außensicht als manuelle Fahrer im Mischverkehr reagieren. Ein weiteres Ziel war es zu verstehen, wie hochautomatisierte Fahrzeuge fahren sollen, damit sich Passagiere sicher fühlen. Aus der Außensicht menschlicher Fahrer untersuchte diese Dissertation den Erstkontakt mit hochautomatisierten Fahrzeugen in dyadischen Interaktionen (Studie 1) und im wiederholten Kontakt (Studie 2) auf längeren Autobahnabschnitten. Die Ergebnisse zeigen, dass Fahrer das regelkonforme hochautomatisierte Fahrverhalten in dyadischen Interaktionen als angenehm und sicher bewerten. Allerdings fühlen sich Fahrer auf längeren Strecken ausgebremst, wodurch ein Gefahrenpotenzial entsteht kann. Weiterhin ist eine Außenkennzeichnung auf längere Sicht zu empfehlen. Aus der Passagiersicht untersuchte diese Dissertation urbane Mischverkehrsinteraktion im longitudinalen Verkehr (Studie 3) und an einer Einmündung (Studie 4). Die Ergebnisse zeigen, dass Passagiere keinerlei Risiko eingehen wollen und sich eine frühzeitige Verhaltensreaktion des hochautomatisierten Fahrzeugs auf schwächere Verkehrsteilnehmer in die Fahrumgebung wünschen. Studienübergreifend zeigt sich, dass hochautomatisierte Fahrzeuge merklich anders fahren, was Passagiere als auch für manuelle Fahrer wahrnehmen können. Automatisiertes Fahrverhalten wird aber maximal als unangenehm, nicht als gefährlich bewertet. Bei der technischen Auslegung automatisierter Fahrfunktionen sollten die Präferenzen von Fahrern und Passagieren gleichermaßen berücksichtigt werden. Zukünftige Studien sollten die Präferenzen anderer menschlicher Verkehrsteilnehmer im Hinblick auf das Verhalten automatisierter Fahrzeuge weiter untersuchen