Motion planning for off-road autonomous driving based on human-like cognition and weight adaptation

• URL: http://arxiv.org/abs/2404.17820v1
• Date: Sat, 27 Apr 2024 08:00:35 GMT
• Title: Motion planning for off-road autonomous driving based on human-like cognition and weight adaptation
• Authors: Yuchun Wang, Cheng Gong, Jianwei Gong, Peng Jia,
• Abstract summary: We propose an adaptive motion planner based on human-like cognition and cost evaluation for off-road driving. We employ a CNN-LSTM network to learn the trajectories planned by human drivers in various off-road scenarios. We show that the proposed human-like motion planner has excellent adaptability to different off-road conditions.
• Score: 9.357567433322766
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Driving in an off-road environment is challenging for autonomous vehicles due to the complex and varied terrain. To ensure stable and efficient travel, the vehicle requires consideration and balancing of environmental factors, such as undulations, roughness, and obstacles, to generate optimal trajectories that can adapt to changing scenarios. However, traditional motion planners often utilize a fixed cost function for trajectory optimization, making it difficult to adapt to different driving strategies in challenging irregular terrains and uncommon scenarios. To address these issues, we propose an adaptive motion planner based on human-like cognition and cost evaluation for off-road driving. First, we construct a multi-layer map describing different features of off-road terrains, including terrain elevation, roughness, obstacle, and artificial potential field map. Subsequently, we employ a CNN-LSTM network to learn the trajectories planned by human drivers in various off-road scenarios. Then, based on human-like generated trajectories in different environments, we design a primitive-based trajectory planner that aims to mimic human trajectories and cost weight selection, generating trajectories that are consistent with the dynamics of off-road vehicles. Finally, we compute optimal cost weights and select and extend behavioral primitives to generate highly adaptive, stable, and efficient trajectories. We validate the effectiveness of the proposed method through experiments in a desert off-road environment with complex terrain and varying road conditions. The experimental results show that the proposed human-like motion planner has excellent adaptability to different off-road conditions. It shows real-time operation, greater stability, and more human-like planning ability in diverse and challenging scenarios.

Related papers

• Hybrid Imitation-Learning Motion Planner for Urban Driving [0.0]
  We propose a novel hybrid motion planner that integrates both learning-based and optimization-based techniques. Our model effectively balances safety and human-likeness, mitigating the trade-off inherent in these objectives. We validate our approach through simulation experiments and further demonstrate its efficacy by deploying it in real-world self-driving vehicles.
  arXiv  Detail & Related papers  (2024-09-04T16:54:31Z)
• Pioneering SE(2)-Equivariant Trajectory Planning for Automated Driving [45.18582668677648]
  Planning the trajectory of the controlled ego vehicle is a key challenge in automated driving. We propose a lightweight equivariant planning model that generates multi-modal joint predictions for all vehicles. We also propose equivariant route attraction to guide the ego vehicle along a high-level route provided by an off-the-shelf GPS navigation system.
  arXiv  Detail & Related papers  (2024-03-17T18:53:46Z)
• Integrating Higher-Order Dynamics and Roadway-Compliance into Constrained ILQR-based Trajectory Planning for Autonomous Vehicles [3.200238632208686]
  Trajectory planning aims to produce a globally optimal route for Autonomous Passenger Vehicles. Existing implementations utilizing the vehicle bicycle kinematic model may not guarantee controllable trajectories. We augment this model by higher-order terms, including the first and second-order derivatives of curvature and longitudinal jerk.
  arXiv  Detail & Related papers  (2023-09-25T22:30:18Z)
• Learning Terrain-Aware Kinodynamic Model for Autonomous Off-Road Rally Driving With Model Predictive Path Integral Control [4.23755398158039]
  We propose a method for learning terrain-aware kinodynamic model conditioned on both proprioceptive and exteroceptive information. The proposed model generates reliable predictions of 6-degree-of-freedom motion and can even estimate contact interactions. We demonstrate the effectiveness of our approach through experiments on a simulated off-road track, showing that our proposed model-controller pair outperforms the baseline.
  arXiv  Detail & Related papers  (2023-05-01T06:09:49Z)
• Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds [100.61456258283245]
  This paper presents a multi-layer motion planning and control architecture for autonomous racing. The proposed solution has been applied on a Dallara AV-21 racecar and tested at oval race tracks achieving lateral accelerations up to 25 $m/s2$.
  arXiv  Detail & Related papers  (2022-07-22T15:16:54Z)
• 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)
• Deep Structured Reactive Planning [94.92994828905984]
  We propose a novel data-driven, reactive planning objective for self-driving vehicles. We show that our model outperforms a non-reactive variant in successfully completing highly complex maneuvers.
  arXiv  Detail & Related papers  (2021-01-18T01:43:36Z)
• 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)
• 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)
• Learning from Naturalistic Driving Data for Human-like Autonomous Highway Driving [11.764518510841235]
  Learning cost parameters of a motion planner from naturalistic driving data is proposed. The learning is achieved by encouraging the selected trajectory to approximate the human driving trajectory under the same traffic situation. Experiments are conducted with respect to both lane change decision and motion planning, and promising results are achieved.
  arXiv  Detail & Related papers  (2020-05-23T04:39:39Z)

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.