Deep Structured Reactive Planning

• URL: http://arxiv.org/abs/2101.06832v1
• Date: Mon, 18 Jan 2021 01:43:36 GMT
• Title: Deep Structured Reactive Planning
• Authors: Jerry Liu, Wenyuan Zeng, Raquel Urtasun, Ersin Yumer
• Abstract summary: 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.
• Score: 94.92994828905984
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: An intelligent agent operating in the real-world must balance achieving its goal with maintaining the safety and comfort of not only itself, but also other participants within the surrounding scene. This requires jointly reasoning about the behavior of other actors while deciding its own actions as these two processes are inherently intertwined - a vehicle will yield to us if we decide to proceed first at the intersection but will proceed first if we decide to yield. However, this is not captured in most self-driving pipelines, where planning follows prediction. In this paper we propose a novel data-driven, reactive planning objective which allows a self-driving vehicle to jointly reason about its own plans as well as how other actors will react to them. We formulate the problem as an energy-based deep structured model that is learned from observational data and encodes both the planning and prediction problems. Through simulations based on both real-world driving and synthetically generated dense traffic, we demonstrate that our reactive model outperforms a non-reactive variant in successfully completing highly complex maneuvers (lane merges/turns in traffic) faster, without trading off collision rate.

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)
• 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)
• SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries [94.84458417662407]
  We introduce SAFE-SIM, a controllable closed-loop safety-critical simulation framework. Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations. We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability.
  arXiv  Detail & Related papers  (2023-12-31T04:14:43Z)
• PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous Driving [57.89801036693292]
  PPAD (Iterative Interaction of Prediction and Planning Autonomous Driving) considers the timestep-wise interaction to better integrate prediction and planning. We design ego-to-agent, ego-to-map, and ego-to-BEV interaction mechanisms with hierarchical dynamic key objects attention to better model the interactions.
  arXiv  Detail & Related papers  (2023-11-14T11:53:24Z)
• Interaction-Aware Decision-Making for Autonomous Vehicles in Forced Merging Scenario Leveraging Social Psychology Factors [7.812717451846781]
  We consider a behavioral model that incorporates both social behaviors and personal objectives of the interacting drivers. We develop a receding-horizon control-based decision-making strategy that estimates online the other drivers' intentions.
  arXiv  Detail & Related papers  (2023-09-25T19:49:14Z)
• The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review [43.30610493968783]
  We review state-of-the-art deep learning-based planning systems, and focus on how they integrate prediction. We discuss the implications, strengths, and limitations of different integration principles.
  arXiv  Detail & Related papers  (2023-08-10T17:53:03Z)
• 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)
• 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)
• End-to-End Interactive Prediction and Planning with Optical Flow Distillation for Autonomous Driving [16.340715765227475]
  We propose an end-to-end interactive neural motion planner (INMP) for autonomous driving in this paper. Our INMP first generates a feature map in bird's-eye-view space, which is then processed to detect other agents and perform interactive prediction and planning jointly. Also, we adopt an optical flow distillation paradigm, which can effectively improve the network performance while still maintaining its real-time inference speed.
  arXiv  Detail & Related papers  (2021-04-18T14:05:18Z)
• 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.