# Towards Full Automated Drive in Urban Environments: A Demonstration in GoMentum Station, California

arXiv: https://arxiv.org/abs/1705.01187 · HTML: https://mlsanity.com/paper/1705.01187/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2017.

## Built on by (most cited first, 17 of 17)

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- [Learning to Navigate Intersections with Unsupervised Driver Trait Inference](https://mlsanity.com/paper/2109.06783/) (2021)
- [Prediction-aware and Reinforcement Learning based Altruistic Cooperative Driving](https://mlsanity.com/paper/2211.10585/) (2022)
- [Reinforcement Learning Based Safe Decision Making for Highway Autonomous Driving](https://mlsanity.com/paper/2105.06517/) (2021)
- [Failure Detection for Motion Prediction of Autonomous Driving: An Uncertainty Perspective](https://mlsanity.com/paper/2301.04421/) (2023)
- [DRNet: A Decision-Making Method for Autonomous Lane Changingwith Deep Reinforcement Learning](https://mlsanity.com/paper/2311.01602/) (2023)
- [Adaptive Decision Making at the Intersection for Autonomous Vehicles Based on Skill Discovery](https://mlsanity.com/paper/2207.11724/) (2022)
- [Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic](https://mlsanity.com/paper/2211.11963/) (2022)
- [AI-Powered CPS-Enabled Vulnerable-User-Aware Urban Transportation Digital Twin: Methods and Applications](https://mlsanity.com/paper/2501.10396/) (2024)
- [AuthSim: Towards Authentic and Effective Safety-critical Scenario Generation for Autonomous Driving Tests](https://mlsanity.com/paper/2502.21100/) (2025)
- [Decision Making for Autonomous Driving in Interactive Merge Scenarios via Learning-based Prediction](https://mlsanity.com/paper/2303.16821/) (2023)

## Abstract (arXiv)

Each year, millions of motor vehicle traffic accidents all over the world cause a large number of fatalities, injuries and significant material loss. Automated Driving (AD) has potential to drastically reduce such accidents. In this work, we focus on the technical challenges that arise from AD in urban environments. We present the overall architecture of an AD system and describe in detail the perception and planning modules. The AD system, built on a modified Acura RLX, was demonstrated in a course in GoMentum Station in California. We demonstrated autonomous handling of 4 scenarios: traffic lights, cross-traffic at intersections, construction zones and pedestrians. The AD vehicle displayed safe behavior and performed consistently in repeated demonstrations with slight variations in conditions. Overall, we completed 44 runs, encompassing 110km of automated driving with only 3 cases where the driver intervened the control of the vehicle, mostly due to error in GPS positioning. Our demonstration showed that robust and consistent behavior in urban scenarios is possible, yet more investigation is necessary for full scale roll-out on public roads.