Integrated Decision and Control: Towards Interpretable and Efficient Driving Intelligence

• URL: http://arxiv.org/abs/2103.10290v1
• Date: Thu, 18 Mar 2021 14:43:31 GMT
• Title: Integrated Decision and Control: Towards Interpretable and Efficient Driving Intelligence
• Authors: Yang Guan, Yangang Ren, Shengbo Eben Li, Haitong Ma, Jingliang Duan, Bo Cheng
• Abstract summary: We present an interpretable and efficient decision and control framework for automated vehicles. It decomposes the driving task into multi-path planning and optimal tracking that are structured hierarchically. Results show that our method has better online computing efficiency and driving performance including traffic efficiency and safety.
• Score: 13.589285628074542
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Decision and control are two of the core functionalities of high-level automated vehicles. Current mainstream methods, such as functionality decomposition or end-to-end reinforcement learning (RL), either suffer high time complexity or poor interpretability and limited safety performance in real-world complex autonomous driving tasks. In this paper, we present an interpretable and efficient decision and control framework for automated vehicles, which decomposes the driving task into multi-path planning and optimal tracking that are structured hierarchically. First, the multi-path planning is to generate several paths only considering static constraints. Then, the optimal tracking is designed to track the optimal path while considering the dynamic obstacles. To that end, in theory, we formulate a constrained optimal control problem (OCP) for each candidate path, optimize them separately and choose the one with the best tracking performance to follow. More importantly, we propose a model-based reinforcement learning (RL) algorithm, which is served as an approximate constrained OCP solver, to unload the heavy computation by the paradigm of offline training and online application. Specifically, the OCPs for all paths are considered together to construct a multi-task RL problem and then solved offline by our algorithm into value and policy networks, for real-time online path selecting and tracking respectively. We verify our framework in both simulation and the real world. Results show that our method has better online computing efficiency and driving performance including traffic efficiency and safety compared with baseline methods. In addition, it yields great interpretability and adaptability among different driving tasks. The real road test also suggests that it is applicable in complicated traffic scenarios without even tuning.

Related papers

• Rethinking Optimal Transport in Offline Reinforcement Learning [64.56896902186126]
  In offline reinforcement learning, the data is provided by various experts and some of them can be sub-optimal. To extract an efficient policy, it is necessary to emphstitch the best behaviors from the dataset. We present an algorithm that aims to find a policy that maps states to a emphpartial distribution of the best expert actions for each given state.
  arXiv  Detail & Related papers  (2024-10-17T22:36:43Z)
• DynamicRouteGPT: A Real-Time Multi-Vehicle Dynamic Navigation Framework Based on Large Language Models [13.33340860174857]
  Real-time dynamic path planning in complex traffic environments presents challenges, such as varying traffic volumes and signal wait times. Traditional static routing algorithms like Dijkstra and A* compute shortest paths but often fail under dynamic conditions. This paper proposes a novel approach based on causal inference for real-time dynamic path planning, balancing global and local optimality.
  arXiv  Detail & Related papers  (2024-08-26T11:19:58Z)
• LLM-A*: Large Language Model Enhanced Incremental Heuristic Search on Path Planning [91.95362946266577]
  Path planning is a fundamental scientific problem in robotics and autonomous navigation. Traditional algorithms like A* and its variants are capable of ensuring path validity but suffer from significant computational and memory inefficiencies as the state space grows. We propose a new LLM based route planning method that synergistically combines the precise pathfinding capabilities of A* with the global reasoning capability of LLMs. This hybrid approach aims to enhance pathfinding efficiency in terms of time and space complexity while maintaining the integrity of path validity, especially in large-scale scenarios.
  arXiv  Detail & Related papers  (2024-06-20T01:24:30Z)
• MARLIN: Soft Actor-Critic based Reinforcement Learning for Congestion Control in Real Networks [63.24965775030673]
  We propose a novel Reinforcement Learning (RL) approach to design generic Congestion Control (CC) algorithms. Our solution, MARLIN, uses the Soft Actor-Critic algorithm to maximize both entropy and return. We trained MARLIN on a real network with varying background traffic patterns to overcome the sim-to-real mismatch.
  arXiv  Detail & Related papers  (2023-02-02T18:27:20Z)
• Bi-Level Optimization Augmented with Conditional Variational Autoencoder for Autonomous Driving in Dense Traffic [0.9281671380673306]
  This paper presents a parameterized bi-level optimization that jointly computes the optimal behavioural decisions and the resulting trajectory. Our approach runs in real-time using a custom GPU-accelerated batch, and a Variational Autoencoder learnt warm-start strategy. Our approach outperforms state-of-the-art model predictive control and RL approaches in terms of collision rate while being competitive in driving efficiency.
  arXiv  Detail & Related papers  (2022-12-05T12:56:42Z)
• CACTO: Continuous Actor-Critic with Trajectory Optimization -- Towards global optimality [5.0915256711576475]
  This paper presents a novel algorithm for the continuous control of dynamical systems that combines Trayy (TO) and Reinforcement Learning (RL) in a single trajectory.
  arXiv  Detail & Related papers  (2022-11-12T10:16:35Z)
• Unified Automatic Control of Vehicular Systems with Reinforcement Learning [64.63619662693068]
  This article contributes a streamlined methodology for vehicular microsimulation. It discovers high performance control strategies with minimal manual design. The study reveals numerous emergent behaviors resembling wave mitigation, traffic signaling, and ramp metering.
  arXiv  Detail & Related papers  (2022-07-30T16:23:45Z)
• Deep Policy Dynamic Programming for Vehicle Routing Problems [89.96386273895985]
  We propose Deep Policy Dynamic Programming (D PDP) to combine the strengths of learned neurals with those of dynamic programming algorithms. D PDP prioritizes and restricts the DP state space using a policy derived from a deep neural network, which is trained to predict edges from example solutions. We evaluate our framework on the travelling salesman problem (TSP) and the vehicle routing problem (VRP) and show that the neural policy improves the performance of (restricted) DP algorithms.
  arXiv  Detail & Related papers  (2021-02-23T15:33:57Z)
• Decision-making for Autonomous Vehicles on Highway: Deep Reinforcement Learning with Continuous Action Horizon [14.059728921828938]
  This paper utilizes the deep reinforcement learning (DRL) method to address the continuous-horizon decision-making problem on the highway. The running objective of the ego automated vehicle is to execute an efficient and smooth policy without collision. The PPO-DRL-based decision-making strategy is estimated from multiple perspectives, including the optimality, learning efficiency, and adaptability.
  arXiv  Detail & Related papers  (2020-08-26T22:49:27Z)
• Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning with Application to Autonomous Driving [35.44498286245894]
  We present an efficient sampling-based maximum-entropy inverse reinforcement learning (IRL) algorithm in this paper. We evaluate the proposed algorithm on real driving data, including both non-interactive and interactive scenarios.
  arXiv  Detail & Related papers  (2020-06-22T01:41:13Z)
• Guided Constrained Policy Optimization for Dynamic Quadrupedal Robot Locomotion [78.46388769788405]
  We introduce guided constrained policy optimization (GCPO), an RL framework based upon our implementation of constrained policy optimization (CPPO) We show that guided constrained RL offers faster convergence close to the desired optimum resulting in an optimal, yet physically feasible, robotic control behavior without the need for precise reward function tuning.
  arXiv  Detail & Related papers  (2020-02-22T10:15:53Z)

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.