Eco-Driving Control of Connected and Automated Vehicles using Neural Network based Rollout

• URL: http://arxiv.org/abs/2310.10878v1
• Date: Mon, 16 Oct 2023 23:13:51 GMT
• Title: Eco-Driving Control of Connected and Automated Vehicles using Neural Network based Rollout
• Authors: Jacob Paugh, Zhaoxuan Zhu, Shobhit Gupta, Marcello Canova, Stephanie Stockar
• Abstract summary: Connected and autonomous vehicles have the potential to minimize energy consumption. Existing deterministic and methods created to solve the eco-driving problem generally suffer from high computational and memory requirements. This work proposes a hierarchical multi-horizon optimization framework implemented via a neural network.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Connected and autonomous vehicles have the potential to minimize energy consumption by optimizing the vehicle velocity and powertrain dynamics with Vehicle-to-Everything info en route. Existing deterministic and stochastic methods created to solve the eco-driving problem generally suffer from high computational and memory requirements, which makes online implementation challenging. This work proposes a hierarchical multi-horizon optimization framework implemented via a neural network. The neural network learns a full-route value function to account for the variability in route information and is then used to approximate the terminal cost in a receding horizon optimization. Simulations over real-world routes demonstrate that the proposed approach achieves comparable performance to a stochastic optimization solution obtained via reinforcement learning, while requiring no sophisticated training paradigm and negligible on-board memory.

Related papers

• End-to-end Driving in High-Interaction Traffic Scenarios with Reinforcement Learning [24.578178308010912]
  We propose an end-to-end model-based RL algorithm named Ramble to address these issues. By learning a dynamics model of the environment, Ramble can foresee upcoming traffic events and make more informed, strategic decisions. Ramble achieves state-of-the-art performance regarding route completion rate and driving score on the CARLA Leaderboard 2.0, showcasing its effectiveness in managing complex and dynamic traffic situations.
  arXiv  Detail & Related papers  (2024-10-03T06:45:59Z)
• Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch [72.26822499434446]
  Auto-Train-Once (ATO) is an innovative network pruning algorithm designed to automatically reduce the computational and storage costs of DNNs. We provide a comprehensive convergence analysis as well as extensive experiments, and the results show that our approach achieves state-of-the-art performance across various model architectures.
  arXiv  Detail & Related papers  (2024-03-21T02:33:37Z)
• Adaptive Resource Allocation for Virtualized Base Stations in O-RAN with Online Learning [60.17407932691429]
  Open Radio Access Network systems, with their base stations (vBSs), offer operators the benefits of increased flexibility, reduced costs, vendor diversity, and interoperability. We propose an online learning algorithm that balances the effective throughput and vBS energy consumption, even under unforeseeable and "challenging'' environments. We prove the proposed solutions achieve sub-linear regret, providing zero average optimality gap even in challenging environments.
  arXiv  Detail & Related papers  (2023-09-04T17:30:21Z)
• Knowledge-Driven Multi-Agent Reinforcement Learning for Computation Offloading in Cybertwin-Enabled Internet of Vehicles [24.29177900273616]
  We propose a knowledge-driven multi-agent reinforcement learning (KMARL) approach to reduce the latency of task offloading in cybertwin-enabled IoV. Specifically, in the considered scenario, the cybertwin serves as a communication agent for each vehicle to exchange information and make offloading decisions in the virtual space.
  arXiv  Detail & Related papers  (2023-08-04T09:11:37Z)
• Tackling Real-World Autonomous Driving using Deep Reinforcement Learning [63.3756530844707]
  In this work, we propose a model-free Deep Reinforcement Learning Planner training a neural network that predicts acceleration and steering angle. In order to deploy the system on board the real self-driving car, we also develop a module represented by a tiny neural network.
  arXiv  Detail & Related papers  (2022-07-05T16:33:20Z)
• Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification [168.67190934250868]
  Federated learning empowered connected autonomous vehicle (FLCAV) has been proposed. FLCAV preserves privacy while reducing communication and annotation costs. It is challenging to determine the network resources and road sensor poses for multi-stage training.
  arXiv  Detail & Related papers  (2022-06-03T23:55:45Z)
• Gradient-Based Trajectory Optimization With Learned Dynamics [80.41791191022139]
  We use machine learning techniques to learn a differentiable dynamics model of the system from data. We show that a neural network can model highly nonlinear behaviors accurately for large time horizons. In our hardware experiments, we demonstrate that our learned model can represent complex dynamics for both the Spot and Radio-controlled (RC) car.
  arXiv  Detail & Related papers  (2022-04-09T22:07:34Z)
• Cooperative Behavioral Planning for Automated Driving using Graph Neural Networks [0.5801044612920815]
  This work proposes to leverage machine learning algorithms to optimize traffic flow at urban intersections by jointly planning for multiple vehicles. Learning-based behavior planning poses several challenges, demanding for a suited input and output representation as well as large amounts of ground-truth data. We train and evaluate the proposed method in an open-source simulation environment for decision making in automated driving.
  arXiv  Detail & Related papers  (2022-02-23T09:36:15Z)
• Offline Contextual Bandits for Wireless Network Optimization [107.24086150482843]
  In this paper, we investigate how to learn policies that can automatically adjust the configuration parameters of every cell in the network in response to the changes in the user demand. Our solution combines existent methods for offline learning and adapts them in a principled way to overcome crucial challenges arising in this context.
  arXiv  Detail & Related papers  (2021-11-11T11:31:20Z)
• Autonomous Navigation through intersections with Graph ConvolutionalNetworks and Conditional Imitation Learning for Self-driving Cars [10.080958939027363]
  In autonomous driving, navigation through unsignaled intersections is a challenging task. We propose a novel branched network G-CIL for the navigation policy learning. Our end-to-end trainable neural network outperforms the baselines with higher success rate and shorter navigation time.
  arXiv  Detail & Related papers  (2021-02-01T07:33:12Z)
• An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet Space [0.0]
  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. The algorithm generates continuoustemporal trajectories on the Frenet frame for the feedback controller to track.
  arXiv  Detail & Related papers  (2020-11-26T02:40:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.