# Augmented Intelligence in Smart Intersections: Local Digital Twins-Assisted Hybrid Autonomous Driving

arXiv: https://arxiv.org/abs/2410.12163 · HTML: https://mlsanity.com/paper/2410.12163/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 16 of its 56 references that are in the map.
- First posted on arXiv 16 Oct 2024.

## Placed in

- End-to-end autonomous driving / [Cooperative and V2X driving](https://mlsanity.com/driving/cooperative-and-v2x-driving/)

## Builds on (most cited first, 16 of 16)

- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [A Minimalist Approach to Offline Reinforcement Learning](https://mlsanity.com/paper/2106.06860/) (2021)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives](https://mlsanity.com/paper/2303.09824/) (2023)
- [How Simulation Helps Autonomous Driving:A Survey of Sim2real, Digital Twins, and Parallel Intelligence](https://mlsanity.com/paper/2305.01263/) (2023)
- [A Tutorial on 5G NR V2X Communications](https://mlsanity.com/paper/2102.04538/) (2021)
- [Interruption-Aware Cooperative Perception for V2X Communication-Aided Autonomous Driving](https://mlsanity.com/paper/2304.11821/) (2023)
- [Providentia -- A Large-Scale Sensor System for the Assistance of Autonomous Vehicles and Its Evaluation](https://mlsanity.com/paper/1906.06789/) (2019)
- [Smart Mobility Digital Twin Based Automated Vehicle Navigation System: A Proof of Concept](https://mlsanity.com/paper/2402.12682/) (2024)
- [Digital Twin-Assisted Cooperative Driving at Non-Signalized Intersections](https://mlsanity.com/paper/2105.01357/) (2021)
- [Boosting Offline Reinforcement Learning for Autonomous Driving with Hierarchical Latent Skills](https://mlsanity.com/paper/2309.13614/) (2023)
- [Demonstrations of Cooperative Perception: Safety and Robustness in Connected and Automated Vehicle Operations](https://mlsanity.com/paper/2011.08581/) (2020)
- [Internet of Federated Digital Twins (IoFDT): Connecting Twins Beyond Borders for Society 5.0](https://mlsanity.com/paper/2312.06432/) (2023)
- [Towards mmWave V2X in 5G and Beyond to Support Automated Driving](https://mlsanity.com/paper/2011.09590/) (2020)
- [Smart Mobility Digital Twin for Automated Driving: Design and Proof-of-Concept](https://mlsanity.com/paper/2401.08654/) (2023)

## Built on by (most cited first, 1 of 1)

- [How Roadside Units Enhance Intersection Safety? Cooperative Autonomous Driving System Design and A Proof of Concept](https://mlsanity.com/paper/2608.09144/) (2026)

## Abstract (arXiv)

Vehicle-road collaboration is a promising approach for enhancing the safety and efficiency of autonomous driving by extending the intelligence of onboard systems to smart roadside infrastructures. The introduction of digital twins (DTs), particularly local DTs (LDTs) at the edge, in smart mobility presents a new embodiment of augmented intelligence, which could enhance information exchange and extract human driving expertise to improve onboard intelligence. This paper presents a novel LDT-assisted hybrid autonomous driving system for improving safety and efficiency in traffic intersections. By leveraging roadside units (RSUs) equipped with sensory and computing capabilities, the proposed system continuously monitors traffic, extracts human driving knowledge, and generates intersection-specific local driving agents through an offline reinforcement learning (RL) framework. When connected and automated vehicles (CAVs) pass through RSU-equipped intersections, RSUs can provide local agents to support safe and efficient driving in local areas. Meanwhile, they provide real-time cooperative perception (CP) to broaden onboard sensory horizons. The proposed LDT-assisted hybrid system is implemented with state-of-the-art products, e.g., CAVs and RSUs, and technologies, e.g., millimeter-wave (mmWave) communications. Hardware-in-the-loop (HiL) simulations and proof-of-concept (PoC) tests validate system performance from two standpoints: (i) The peak latency for CP and local agent downloading are 8.51 ms and 146 ms, respectively, aligning with 3GPP requirements for vehicle-to-everything (V2X) and model transfer use cases. Moreover, (ii) local driving agents can improve safety measures by 10% and reduce travel time by 15% compared with conventional onboard systems. The implemented prototype also demonstrates reliable real-time performance, fulfilling the targets of the proposed system design.