Learning from Experience for Rapid Generation of Local Car Maneuvers

• URL: http://arxiv.org/abs/2012.03707v1
• Date: Mon, 7 Dec 2020 14:05:45 GMT
• Title: Learning from Experience for Rapid Generation of Local Car Maneuvers
• Authors: Piotr Kicki, Tomasz Gawron, Krzysztof \'Cwian, Mete Ozay, Piotr Skrzypczy\'nski
• Abstract summary: We propose to train a deep neural network (DNN) to plan feasible and nearly-optimal paths for kinematically constrained vehicles. Our model is trained using a novel weakly supervised approach and a gradient-based policy search. While the path generation time is about 40 ms, the generated paths are smooth and comparable to those obtained from conventional path planners.
• Score: 9.621143711575543
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Being able to rapidly respond to the changing scenes and traffic situations by generating feasible local paths is of pivotal importance for car autonomy. We propose to train a deep neural network (DNN) to plan feasible and nearly-optimal paths for kinematically constrained vehicles in small constant time. Our DNN model is trained using a novel weakly supervised approach and a gradient-based policy search. On real and simulated scenes and a large set of local planning problems, we demonstrate that our approach outperforms the existing planners with respect to the number of successfully completed tasks. While the path generation time is about 40 ms, the generated paths are smooth and comparable to those obtained from conventional path planners.

Related papers

• Speeding Up Path Planning via Reinforcement Learning in MCTS for Automated Parking [3.750010944080163]
  We propose a reinforcement learning pipeline with a Monte Carlo tree search under the path planning framework. By iteratively learning the value of a state, we are able to model a value estimator and a policy generator for given states.
  arXiv  Detail & Related papers  (2024-03-25T22:21:23Z)
• Rethinking Closed-loop Training for Autonomous Driving [82.61418945804544]
  We present the first empirical study which analyzes the effects of different training benchmark designs on the success of learning agents. We propose trajectory value learning (TRAVL), an RL-based driving agent that performs planning with multistep look-ahead. Our experiments show that TRAVL can learn much faster and produce safer maneuvers compared to all the baselines.
  arXiv  Detail & Related papers  (2023-06-27T17:58:39Z)
• FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing [71.76084256567599]
  We present a system that enables an autonomous small-scale RC car to drive aggressively from visual observations using reinforcement learning (RL) Our system, FastRLAP (faster lap), trains autonomously in the real world, without human interventions, and without requiring any simulation or expert demonstrations. The resulting policies exhibit emergent aggressive driving skills, such as timing braking and acceleration around turns and avoiding areas which impede the robot's motion, approaching the performance of a human driver using a similar first-person interface over the course of training.
  arXiv  Detail & Related papers  (2023-04-19T17:33:47Z)
• Speeding up deep neural network-based planning of local car maneuvers via efficient B-spline path construction [3.9596068699962323]
  We introduce a novel B-spline path construction method, making it possible to generate local maneuvers in almost constant time of about 11 ms. We evaluate thoroughly the new planner employing the recent Bench-MR framework to obtain quantitative results showing that our method outperforms state-of-the-art planners by a large margin.
  arXiv  Detail & Related papers  (2022-03-14T09:54:24Z)
• Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Prior [135.78858513845233]
  STRIVE is a method to automatically generate challenging scenarios that cause a given planner to produce undesirable behavior, like collisions. To maintain scenario plausibility, the key idea is to leverage a learned model of traffic motion in the form of a graph-based conditional VAE. A subsequent optimization is used to find a "solution" to the scenario, ensuring it is useful to improve the given planner.
  arXiv  Detail & Related papers  (2021-12-09T18:03:27Z)
• End-to-end Interpretable Neural Motion Planner [78.69295676456085]
  We propose a neural motion planner (NMP) for learning to drive autonomously in complex urban scenarios. We design a holistic model that takes as input raw LIDAR data and a HD map and produces interpretable intermediate representations. We demonstrate the effectiveness of our approach in real-world driving data captured in several cities in North America.
  arXiv  Detail & Related papers  (2021-01-17T14:16:12Z)
• 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)
• Trajectory Planning for Autonomous Vehicles Using Hierarchical Reinforcement Learning [21.500697097095408]
  Planning safe trajectories under uncertain and dynamic conditions makes the autonomous driving problem significantly complex. Current sampling-based methods such as Rapidly Exploring Random Trees (RRTs) are not ideal for this problem because of the high computational cost. We propose a Hierarchical Reinforcement Learning structure combined with a Proportional-Integral-Derivative (PID) controller for trajectory planning.
  arXiv  Detail & Related papers  (2020-11-09T20:49:54Z)
• Path Planning Followed by Kinodynamic Smoothing for Multirotor Aerial Vehicles (MAVs) [61.94975011711275]
  We propose a geometrically based motion planning technique textquotedblleft RRT*textquotedblright; for this purpose. In the proposed technique, we modified original RRT* introducing an adaptive search space and a steering function. We have tested the proposed technique in various simulated environments.
  arXiv  Detail & Related papers  (2020-08-29T09:55:49Z)
• A Self-Supervised Learning Approach to Rapid Path Planning for Car-Like Vehicles Maneuvering in Urban Environment [3.867363075280544]
  We introduce a novel neural network-based method for path planning, which employs a gradient-based self-supervised learning algorithm to predict feasible paths. This approach strongly exploits the experience gained in the past and rapidly yields feasible maneuver plans for car-like vehicles with limited steering-angle.
  arXiv  Detail & Related papers  (2020-03-02T14:48:29Z)
• Intelligent Roundabout Insertion using Deep Reinforcement Learning [68.8204255655161]
  We present a maneuver planning module able to negotiate the entering in busy roundabouts. The proposed module is based on a neural network trained to predict when and how entering the roundabout throughout the whole duration of the maneuver.
  arXiv  Detail & Related papers  (2020-01-03T11:16:41Z)

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.