# An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet Space

arXiv: https://arxiv.org/abs/2011.13098 · HTML: https://mlsanity.com/paper/2011.13098/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Driving](https://mlsanity.com/paper/2405.01440/) (2024)
- [Automatic Curriculum Learning for Driving Scenarios: Towards Robust and Efficient Reinforcement Learning](https://mlsanity.com/paper/2505.08264/) (2025)
- [A Safe and Efficient Self-evolving Algorithm for Decision-making and Control of Autonomous Driving Systems](https://mlsanity.com/paper/2408.12187/) (2024)
- [A Safe Self-evolution Algorithm for Autonomous Driving Based on Data-Driven Risk Quantification Model](https://mlsanity.com/paper/2408.12805/) (2024)
- [Zero-shot Transfer Learning of Driving Policy via Socially Adversarial Traffic Flow](https://mlsanity.com/paper/2304.12821/) (2023)

## Abstract (arXiv)

Tactical decision making and strategic motion planning for autonomous highway driving are challenging due to the complication of predicting other road users' behaviors, diversity of environments, and complexity of the traffic interactions. This paper presents a novel end-to-end continuous deep reinforcement learning approach towards autonomous cars' decision-making and motion planning. For the first time, we define both states and action spaces on the Frenet space to make the driving behavior less variant to the road curvatures than the surrounding actors' dynamics and traffic interactions. The agent receives time-series data of past trajectories of the surrounding vehicles and applies convolutional neural networks along the time channels to extract features in the backbone. The algorithm generates continuous spatiotemporal trajectories on the Frenet frame for the feedback controller to track. Extensive high-fidelity highway simulations on CARLA show the superiority of the presented approach compared with commonly used baselines and discrete reinforcement learning on various traffic scenarios. Furthermore, the proposed method's advantage is confirmed with a more comprehensive performance evaluation against 1000 randomly generated test scenarios.