Goal-Directed Occupancy Prediction for Lane-Following Actors

• URL: http://arxiv.org/abs/2009.12174v1
• Date: Sun, 6 Sep 2020 20:44:59 GMT
• Title: Goal-Directed Occupancy Prediction for Lane-Following Actors
• Authors: Poornima Kaniarasu, Galen Clark Haynes, Micol Marchetti-Bowick
• Abstract summary: Predicting the possible future behaviors of vehicles that drive on shared roads is a crucial task for safe autonomous driving. We propose a new method that leverages the mapped road topology to reason over possible goals and predict the future spatial occupancy of dynamic road actors.
• Score: 5.469556349325342
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predicting the possible future behaviors of vehicles that drive on shared roads is a crucial task for safe autonomous driving. Many existing approaches to this problem strive to distill all possible vehicle behaviors into a simplified set of high-level actions. However, these action categories do not suffice to describe the full range of maneuvers possible in the complex road networks we encounter in the real world. To combat this deficiency, we propose a new method that leverages the mapped road topology to reason over possible goals and predict the future spatial occupancy of dynamic road actors. We show that our approach is able to accurately predict future occupancy that remains consistent with the mapped lane geometry and naturally captures multi-modality based on the local scene context while also not suffering from the mode collapse problem observed in prior work.

Related papers

• QuAD: Query-based Interpretable Neural Motion Planning for Autonomous Driving [33.609780917199394]
  Self-driving vehicles must understand its environment to determine appropriate action. Traditional systems rely on object detection to find agents in the scene. We present a unified, interpretable, and efficient autonomy framework that moves away from cascading modules that first perceive occupancy relevant-temporal autonomy.
  arXiv  Detail & Related papers  (2024-04-01T21:11:43Z)
• Tractable Joint Prediction and Planning over Discrete Behavior Modes for Urban Driving [15.671811785579118]
  We show that we can parameterize autoregressive closed-loop models without retraining. We propose fully reactive closed-loop planning over discrete latent modes. Our approach also outperforms the previous state-of-the-art in CARLA on challenging dense traffic scenarios.
  arXiv  Detail & Related papers  (2024-03-12T01:00:52Z)
• Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving [68.95178518732965]
  A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants. Existing works either perform object detection followed by trajectory of the detected objects, or predict dense occupancy and flow grids for the whole scene. This motivates our unified approach to perception and future prediction that implicitly represents occupancy and flow over time with a single neural network.
  arXiv  Detail & Related papers  (2023-08-02T23:39:24Z)
• Transferable and Adaptable Driving Behavior Prediction [34.606012573285554]
  We propose HATN, a hierarchical framework to generate high-quality, transferable, and adaptable predictions for driving behaviors. We demonstrate our algorithms in the task of trajectory prediction for real traffic data at intersections and roundabouts from the INTERACTION dataset.
  arXiv  Detail & Related papers  (2022-02-10T16:46:24Z)
• Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving [104.32241082170044]
  We study a new task, safety-aware motion prediction with unseen vehicles for autonomous driving. Unlike the existing trajectory prediction task for seen vehicles, we aim at predicting an occupancy map. Our approach is the first one that can predict the existence of unseen vehicles in most cases.
  arXiv  Detail & Related papers  (2021-09-03T13:33:33Z)
• Contingencies from Observations: Tractable Contingency Planning with Learned Behavior Models [82.34305824719101]
  Humans have a remarkable ability to make decisions by accurately reasoning about future events. We develop a general-purpose contingency planner that is learned end-to-end using high-dimensional scene observations. We show how this model can tractably learn contingencies from behavioral observations.
  arXiv  Detail & Related papers  (2021-04-21T14:30:20Z)
• Multi-modal Trajectory Prediction for Autonomous Driving with Semantic Map and Dynamic Graph Attention Network [12.791191495432829]
  There are several challenges in trajectory prediction in real-world traffic scenarios. Inspired by people's natural habit of navigating traffic with attention to their goals and surroundings, this paper presents a unique graph attention network. The network is designed to model the dynamic social interactions among agents and conform to traffic rules with a semantic map.
  arXiv  Detail & Related papers  (2021-03-30T11:53:12Z)
• 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)
• What-If Motion Prediction for Autonomous Driving [58.338520347197765]
  Viable solutions must account for both the static geometric context, such as road lanes, and dynamic social interactions arising from multiple actors. We propose a recurrent graph-based attentional approach with interpretable geometric (actor-lane) and social (actor-actor) relationships. Our model can produce diverse predictions conditioned on hypothetical or "what-if" road lanes and multi-actor interactions.
  arXiv  Detail & Related papers  (2020-08-24T17:49:30Z)
• The Importance of Prior Knowledge in Precise Multimodal Prediction [71.74884391209955]
  Roads have well defined geometries, topologies, and traffic rules. In this paper we propose to incorporate structured priors as a loss function. We demonstrate the effectiveness of our approach on real-world self-driving datasets.
  arXiv  Detail & Related papers  (2020-06-04T03:56:11Z)

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.