# An Intelligent Self-driving Truck System For Highway Transportation

arXiv: https://arxiv.org/abs/2112.15304 · HTML: https://mlsanity.com/paper/2112.15304/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in End-to-end system architecture, one of 9 directions in End-to-end autonomous driving.
- Builds on 14 of its 43 references that are in the map.
- First posted on arXiv 31 Dec 2021.

## Placed in

- End-to-end autonomous driving / [End-to-end system architecture](https://mlsanity.com/driving/end-to-end-system-architecture/)

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [End-to-end Driving via Conditional Imitation Learning](https://mlsanity.com/paper/1710.02410/) (2017)
- [Deep Reinforcement Learning framework for Autonomous Driving](https://mlsanity.com/paper/1704.02532/) (2017)
- [A Reinforcement Learning Based Approach for Automated Lane Change Maneuvers](https://mlsanity.com/paper/1804.07871/) (2018)
- [SUMMIT: A Simulator for Urban Driving in Massive Mixed Traffic](https://mlsanity.com/paper/1911.04074/) (2019)
- [Lane Change Decision-making through Deep Reinforcement Learning with Rule-based Constraints](https://mlsanity.com/paper/1904.00231/) (2019)
- [Safe Occlusion-aware Autonomous Driving via Game-Theoretic Active Perception](https://mlsanity.com/paper/2105.08169/) (2021)
- [A Regularized and Smoothed Fischer-Burmeister Method for Quadratic Programming with Applications to Model Predictive Control](https://mlsanity.com/paper/1807.03214/) (2018)
- [Autonomous Driving at Intersections: A Critical-Turning-Point Approach for Left Turns](https://mlsanity.com/paper/2003.02409/) (2020)
- [Distributionally Consistent Simulation of Naturalistic Driving Environment for Autonomous Vehicle Testing](https://mlsanity.com/paper/2101.02828/) (2021)

## Built on by (most cited first, 3 of 3)

- [ESP: Extro-Spective Prediction for Long-term Behavior Reasoning in Emergency Scenarios](https://mlsanity.com/paper/2405.04100/) (2024)
- [Autonomous Navigation of Tractor-Trailer Vehicles through Roundabout Intersections](https://mlsanity.com/paper/2401.04980/) (2024)
- [nuTruck: Benchmarking Autonomous Driving Planning for Distributed Electric-drive Trucks](https://mlsanity.com/paper/2607.13704/) (2026)

## Abstract (arXiv)

Recently, there have been many advances in autonomous driving society, attracting a lot of attention from academia and industry. However, existing works mainly focus on cars, extra development is still required for self-driving truck algorithms and models. In this paper, 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, 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 shows our system's capacity of mitigating sim-to-real gap. Our code is available at https://github.com/InceptioResearch/IITS