Intelligent Transportation

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3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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End-to-End Learning vs. Modular Architectures: Comparative Insights into Autonomous Driving Systems
Oct 1, 2026Kartik B. KapseCausality-Aware End-To-End Autonomous Driving Intelligent Transportation
AI Engineering of Autonomous Systems, Dept. Electrical Engineering and IT, Technische Hochschule Ingolstadt
Trustworthy Data- and ML-Ops for Intelligent Transportation Systems and Logistics
Oct 1, 2026Antonio Emanuele Cinà, Giovanni Scodeller, Cecilia Caterina Pasquale +5Intelligent Transportation Logistics
Agent-Based Modeling of Systems of Systems
Sep 23, 2026Jean-Baptiste Soyez, Gildas Morvan, Rochdi Merzouki +1Agent-Based Model Intelligent Transportation
On the security and privacy of LLMs in Mobility
Sep 22, 2026Mauro Conti, Lorenzo Perinello, Umberto SalviatiIntelligent Transportation Mobility
University of Padova Italy · Fondazione Bruno Kessler Italy
A traffic management system for large and heterogeneous vehicles in narrow industrial environments
Sep 9, 2026Alessandro Bonetti, Silvia Proia, Simone Guidetti +1Intelligent Transportation Multi-Agent Path Finding
Department of Sciences and Methods for Engineering (DISMI), University of Modena and Reggio Emilia, Italy · Gruppo TecnoFerrari S.p.a. con socio unico, Italy
UniTraffic-Agent: Unified Traffic Video Reasoning for AI City Challenge 2026 Track 3 with Two Out-of-Domain Evaluations
Aug 13, 2026Peng Li, Qianqian Xu, Shilong Bao +2Intelligent Transportation Fine-Grained Video Understanding
School of Computer Science and Technology, University of Chinese Academy of Sciences (UCAS), Beijing, China · State Key Laboratory of AI Safety, Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS), Beijing, China · Beijing Academy of Artificial Intelligence (BAAI), Beijing, China +1
Decentralized Multi-Agent Urban Traffic Management via Spatio-Temporal Mobility Profile Planning
Aug 8, 2026Lorenzo Mario Amorosa, Lorenzo Farina, Vittorio Todisco +1Intelligent Transportation Mobility
Department of Electrical, Electronic and Information Engineering (DEI), “Guglielmo Marconi”, University of Bologna & WiLab - National Wireless Communications Laboratory of CNIT, Bologna, Italy.
PRISA: Proactive Infrastructure LiDAR Framework for Intersection Safety Assessment
Jul 17, 2026Tam Bang, Hussam Abubakr, Emiliano de la Garza Villarreal +6Intelligent Transportation
Center for Urban Informatics and Progress, University of Tennessee at Chattanooga, USA
Large Language Model Enhanced Differentiable Trajectory Planning for IoT-Enabled Autonomous Driving
Jul 11, 2026Shihao Zhang, Jing Yang, Ziyu Song +7Automated Vehicles Intelligent Transportation
State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130000, China · Center of Research for Cyber Security and Network (CSNET), Faculty of Computer Science and Information Technology, Universiti Malaya, 50603 Kuala Lumpur, Malaysia · Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg +3
Autonomous discovery of traffic laws with AI traffic scientists
Jul 2, 2026Xingyuan Dai, Yue Liu, Xiaoyan Gong +9Intelligent Transportation Traffic Sign Recognition
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. · School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China. · China Telecom Research Institute, Beijing, 102209, China. +2
Revealing Safety-Critical Scenarios for UTM via Transformer
Jun 30, 2026Huaze Tang, Bill Zeng, Chao Wang +3Safety-Critical Scenarios Intelligent Transportation
Tsinghua University · Meituan
When Stopping Fails: Rethinking Minimal Risk Conditions through Human-Interactive Autonomous Driving for Safe Transportation Systems
Jun 27, 2026Yash Tandon, Giovanni Tapia Lopez, Marcus Blennemann +2Automated Vehicles Collision Avoidance
UC San Diego · UC Merced
Decentralized Coordination of Autonomous Traffic Through Advanced Air Mobility Corridors
Jun 22, 2026Jasmine Jerry Aloor, Hamsa BalakrishnanAir Traffic Control Intelligent Transportation
Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Decentralized Autonomous Traffic Management through Corridor Networks
Jun 22, 2026Jasmine Jerry Aloor, Aadarsh Govada, Hamsa BalakrishnanIntelligent Transportation Traffic Signal Control
Department of Aeronautics and Astronautics · Massachusetts Institute of Technology · Cambridge, MA, USA +2
Effects of Social Interactions in Self-Organising Railway Traffic Management
Jun 11, 2026Fabio Oddi, Federico Naldini, Leo D'Amato +3Intelligent Transportation Short-Horizon Forecasting
Institute for Cognitive Sciences and Technologies, CNR, Via Giandomenico Romagnosi 18A, 00196, Rome, Italy · Dipartimento di Ingegneria Informatica, Automatica e Gestionale, La Sapienza Universit´a di Roma, Via Ariosto 25, 00185, Rome, Italy · Univ. Gustave Eiffel, COSYS-ESTAS, F-59650 Villeneuve d’Ascq, France +1
Selecting New Measurement Locations to Diversify Traffic-Pattern Coverage: A Real-World Evaluation for Total Traffic Volume Estimation
May 25, 2026Masaaki Inoue, Akifumi Okuno, Shintaro FukushimaAccurate Traffic Forecasting Traffic
1TOYOTA Motor Corporation · Institute of Statistical Mathematics · 3The Graduate University for Advanced Studies, SOKENDAI +1
Secure Coordination for Vertiport Sequencing in Advanced Air Mobility
May 20, 2026Jaehan Im, Filippos Fotiadis, Ufuk Topcu +1Cooperative Air Combat Intelligent Transportation
Graduate Research Assistant, Department of Aerospace Engineering and Engineering Mechanics · Postdoctoral Researcher, Oden Institute for Computational Engineering & Sciences · Professor, Oden Institute for Computational Engineering & Sciences +1
Time-To-Reach Separation and Safety Filtering for Safe, Fair, and Efficient Multi-Agent Coordination
May 20, 2026Matthew Low, Jasmine Jerry Aloor, Victoria Marie Tuck +2Air Traffic Control Intelligent Transportation
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley · Department of Aeronautics and Astronautics, Massachusetts Institute of Technology · GRASP Laboratory, University of Pennsylvania +1
Intent-First Aerial V2V for Tactical Coordination and Separation: Protocol and Performance Under Density and Disturbance
May 20, 2026Mehrnaz SabetCooperative Air Combat Air Traffic Control
Cornell University, Ithaca, NY 14853 USA
Multi-Pedestrian Safety Warning at Urban Intersections Use Case of Digital Twin
May 12, 2026Yongjie Fu, Qi Gao, Mahshid Ghasemi Dehkordi +2Digital Twins Pedestrian
Department of Civil Engineering and Engineering Mechanics at Columbia University, New York. · Mahshid Ghasemi Dehkordi and Gil Zussman are with the Department of Electrical Engineering at Columbia University, New York. · Yongjie Fu, Qi Gao, and Xuan Di are with the Department of Civil Engineering and Engineering Mechanics at Columbia University, New York. +1
Balancing Efficiency and Fairness in Traffic Light Control through Deep Reinforcement Learning
May 11, 2026Matteo Cederle, Giacomo Scatto, Gian Antonio SustoTraffic Signal Control Intelligent Transportation
University of Padova, Italy
A Comparative Analysis of Machine Learning Models for Intrusion Detection in Intelligent Transport Systems
Apr 30, 2026Zawad Yalmie Sazid, Robert Abbas, Sasa MaricEdge Devices Intrusion Detection
Victoria University, Sydney, Australia · University of New South Wales College, Sydney, Australia
karl. -- A Research Vehicle for Automated and Connected Driving
Feb 9, 2026Jean-Pierre Busch, Lukas Ostendorf, Guido Linden +5Automated Vehicles Intelligent Transportation
Institute for Automotive Engineering (ika), RWTH Aachen University, Germany
Event-based vision sensing and its application to pedestrian detection for intelligent transportation and surveillance
Jul 5, 2024Han Wang, Juntao Wu, Jingyuan Bao +5Pedestrian Detection Vision Sensors
Instance-Conditioned Adaptation for Large-scale Generalization of Neural Routing Solver
May 3, 2024Changliang Zhou, Xi Lin, Zhenkun Wang +3Vehicle Routing Problem Intelligent Transportation
Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology, School of Automation and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen 518055, China · School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710049, China · Huawei Noah’s Ark Lab, Hong Kong SAR, China +1

Oct 1, 2026, cs.ROJ/K move · Enter open · S save

End-to-End Learning vs. Modular Architectures: Comparative Insights into Autonomous Driving Systems

Kartik B. Kapse

AI Engineering of Autonomous Systems, Dept. Electrical Engineering and IT, Technische Hochschule Ingolstadt

Causality-Aware End-To-End Autonomous Driving Intelligent Transportation Modular Framework Deep Learning Comparison

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Autonomous driving systems have become a central focus of intelligent transportation research, with End-to-End Learning and Modular Architectures offering two prominent design paradigms for their implementation. E2E Learning uses deep learning algorithms to map raw sensory inputs directly to driving actuators, providing a streamlined and adaptable solution. while Modular Architectures employ a pipeline-based approach, dividing the system into distinct subsystems for perception, cognition, planning, and control. This paper presents a comprehensive comparative analysis of these paradigms, focusing on their strengths, limitations, and trade-offs to provide insights into their suitability for various autonomous driving applications. The study evaluates key factors such as interpretability, scalability, robustness, and real-world applicability. While End-to-End Learning emphasizes simplicity and adaptability in dynamic environments, it lacks transparency and is highly dependent on large datasets. Conversely, Modular Architectures offer superior interpretability and task-specific optimization, but face challenges related to integration complexity and scalability. To address these limitations, hybrid approaches that combine the strengths of both paradigms have emerged, offering a promising direction for overcoming these challenges. Beyond this comparative synthesis, following work proposes a Four-Dimensional Architecture Selection Framework, comprising twelve binary criteria across safety, operating environment, data/computational resources, and deployment context, and validate it against ten published autonomous driving systems, correctly recommending 7/10 deployed architectures. This work synthesizes existing literature to highlight key trade-offs between the paradigms and identifies hybrid architectures as a promising direction for future research.

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