Multi-Agent Variational Occlusion Inference Using People as Sensors

• URL: http://arxiv.org/abs/2109.02173v1
• Date: Sun, 5 Sep 2021 21:56:54 GMT
• Title: Multi-Agent Variational Occlusion Inference Using People as Sensors
• Authors: Masha Itkina, Ye-Ji Mun, Katherine Driggs-Campbell, and Mykel J. Kochenderfer
• Abstract summary: Inferring occupancy from agent behaviors is an inherently multimodal problem. We propose an occlusion inference method that characterizes observed behaviors of human agents as sensor measurements. Our approach is validated on a real-world dataset, outperforming baselines and demonstrating real-time capable performance.
• Score: 28.831182328958477
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous vehicles must reason about spatial occlusions in urban environments to ensure safety without being overly cautious. Prior work explored occlusion inference from observed social behaviors of road agents. Inferring occupancy from agent behaviors is an inherently multimodal problem; a driver may behave in the same manner for different occupancy patterns ahead of them (e.g., a driver may move at constant speed in traffic or on an open road). Past work, however, does not account for this multimodality, thus neglecting to model this source of aleatoric uncertainty in the relationship between driver behaviors and their environment. We propose an occlusion inference method that characterizes observed behaviors of human agents as sensor measurements, and fuses them with those from a standard sensor suite. To capture the aleatoric uncertainty, we train a conditional variational autoencoder with a discrete latent space to learn a multimodal mapping from observed driver trajectories to an occupancy grid representation of the view ahead of the driver. Our method handles multi-agent scenarios, combining measurements from multiple observed drivers using evidential theory to solve the sensor fusion problem. Our approach is validated on a real-world dataset, outperforming baselines and demonstrating real-time capable performance. Our code is available at https://github.com/sisl/MultiAgentVariationalOcclusionInference .

Related papers

• 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)
• Decision Making for Autonomous Driving in Interactive Merge Scenarios via Learning-based Prediction [39.48631437946568]
  This paper focuses on the complex task of merging into moving traffic where uncertainty emanates from the behavior of other drivers. We frame the problem as a partially observable Markov decision process (POMDP) and solve it online with Monte Carlo tree search. The solution to the POMDP is a policy that performs high-level driving maneuvers, such as giving way to an approaching car, keeping a safe distance from the vehicle in front or merging into traffic.
  arXiv  Detail & Related papers  (2023-03-29T16:12:45Z)
• Anomaly Detection in Multi-Agent Trajectories for Automated Driving [2.5211566369910967]
  Similar to humans, automated vehicles are supposed to perform anomaly detection. Our innovation is the ability to jointly learn multiple trajectories of a dynamic number of agents.
  arXiv  Detail & Related papers  (2021-10-15T08:07:31Z)
• Calibration of Human Driving Behavior and Preference Using Naturalistic Traffic Data [5.926030548326619]
  We show how the model can be inverted to estimate driver preferences from naturalistic traffic data. One distinct advantage of our approach is the drastically reduced computational burden.
  arXiv  Detail & Related papers  (2021-05-05T01:20:03Z)
• End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning [63.56464608571663]
  Navigating through intersections is one of the main challenging tasks for an autonomous vehicle. In this work, we focus on the implementation of a system able to navigate through intersections where only traffic signs are provided. We propose a multi-agent system using a continuous, model-free Deep Reinforcement Learning algorithm used to train a neural network for predicting both the acceleration and the steering angle at each time step.
  arXiv  Detail & Related papers  (2021-04-28T07:54:40Z)
• Multi-Modal Fusion Transformer for End-to-End Autonomous Driving [59.60483620730437]
  We propose TransFuser, a novel Multi-Modal Fusion Transformer, to integrate image and LiDAR representations using attention. Our approach achieves state-of-the-art driving performance while reducing collisions by 76% compared to geometry-based fusion.
  arXiv  Detail & Related papers  (2021-04-19T11:48:13Z)
• IntentNet: Learning to Predict Intention from Raw Sensor Data [86.74403297781039]
  In this paper, we develop a one-stage detector and forecaster that exploits both 3D point clouds produced by a LiDAR sensor as well as dynamic maps of the environment. Our multi-task model achieves better accuracy than the respective separate modules while saving computation, which is critical to reducing reaction time in self-driving applications.
  arXiv  Detail & Related papers  (2021-01-20T00:31:52Z)
• PRANK: motion Prediction based on RANKing [4.4861975043227345]
  Predicting the motion of agents is one of the most critical problems in the autonomous driving domain. We introduce the PRANK method, which produces the conditional distribution of agent's trajectories plausible in the given scene. We evaluate PRANK on the in-house and Argoverse datasets, where it shows competitive results.
  arXiv  Detail & Related papers  (2020-10-22T19:58:02Z)
• Studying Person-Specific Pointing and Gaze Behavior for Multimodal Referencing of Outside Objects from a Moving Vehicle [58.720142291102135]
  Hand pointing and eye gaze have been extensively investigated in automotive applications for object selection and referencing. Existing outside-the-vehicle referencing methods focus on a static situation, whereas the situation in a moving vehicle is highly dynamic and subject to safety-critical constraints. We investigate the specific characteristics of each modality and the interaction between them when used in the task of referencing outside objects.
  arXiv  Detail & Related papers  (2020-09-23T14:56:19Z)
• Diverse and Admissible Trajectory Forecasting through Multimodal Context Understanding [46.52703817997932]
  Multi-agent trajectory forecasting in autonomous driving requires an agent to accurately anticipate the behaviors of the surrounding vehicles and pedestrians. We propose a model that synthesizes multiple input signals from the multimodal world. We show a significant performance improvement over previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-03-06T13:59:39Z)

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.