Fast and Accurate Collision Probability Estimation for Autonomous Vehicles using Adaptive Sigma-Point Sampling

• URL: http://arxiv.org/abs/2507.06149v1
• Date: Tue, 08 Jul 2025 16:31:11 GMT
• Title: Fast and Accurate Collision Probability Estimation for Autonomous Vehicles using Adaptive Sigma-Point Sampling
• Authors: Charles Champagne Cossette, Taylor Scott Clawson, Andrew Feit,
• Abstract summary: We propose an adaptive sigma-point sampling scheme, which produces a fast, simple algorithm capable of estimating the collision probability with a median error of 3.5%.<n> Importantly, the algorithm explicitly accounts for the collision probability's temporal dependence, which is often neglected in prior work.<n>The method is tested on a diverse set of relevant real-world scenarios, consisting of 400 6-second snippets of autonomous vehicle logs.
• Score: 0.9217021281095907
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: A novel algorithm is presented for the estimation of collision probabilities between dynamic objects with uncertain trajectories, where the trajectories are given as a sequence of poses with Gaussian distributions. We propose an adaptive sigma-point sampling scheme, which ultimately produces a fast, simple algorithm capable of estimating the collision probability with a median error of 3.5%, and a median runtime of 0.21ms, when measured on an Intel Xeon Gold 6226R Processor. Importantly, the algorithm explicitly accounts for the collision probability's temporal dependence, which is often neglected in prior work and otherwise leads to an overestimation of the collision probability. Finally, the method is tested on a diverse set of relevant real-world scenarios, consisting of 400 6-second snippets of autonomous vehicle logs, where the accuracy and latency is rigorously evaluated.

Related papers

• Event-Aided Time-to-Collision Estimation for Autonomous Driving [28.13397992839372]
  We present a novel method that estimates the time to collision using a neuromorphic event-based camera. The proposed algorithm consists of a two-step approach for efficient and accurate geometric model fitting on event data. Experiments on both synthetic and real data demonstrate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2024-07-10T02:37:36Z)
• A 5-Point Minimal Solver for Event Camera Relative Motion Estimation [47.45081895021988]
  We introduce a novel minimal 5-point solver that estimates line parameters and linear camera velocity projections, which can be fused into a single, averaged linear velocity when considering multiple lines. Our method consistently achieves a 100% success rate in estimating linear velocity where existing closed-form solvers only achieve between 23% and 70%.
  arXiv  Detail & Related papers  (2023-09-29T08:30:18Z)
• Learning Unnormalized Statistical Models via Compositional Optimization [73.30514599338407]
  Noise-contrastive estimation(NCE) has been proposed by formulating the objective as the logistic loss of the real data and the artificial noise. In this paper, we study it a direct approach for optimizing the negative log-likelihood of unnormalized models.
  arXiv  Detail & Related papers  (2023-06-13T01:18:16Z)
• MonoPIC -- A Monocular Low-Latency Pedestrian Intention Classification Framework for IoT Edges Using ID3 Modelled Decision Trees [0.0]
  We propose an algorithm that classifies the intent of a single arbitrarily chosen pedestrian in a two dimensional frame into logic states. This bypasses the need to employ any relatively high latency deep-learning algorithms. The model was able to achieve an average testing accuracy of 83.56% with a reliable variance of 0.0042 while operating with an average latency of 48 milliseconds.
  arXiv  Detail & Related papers  (2023-04-01T02:42:24Z)
• Particle-Based Score Estimation for State Space Model Learning in Autonomous Driving [62.053071723903834]
  Multi-object state estimation is a fundamental problem for robotic applications. We consider learning maximum-likelihood parameters using particle methods. We apply our method to real data collected from autonomous vehicles.
  arXiv  Detail & Related papers  (2022-12-14T01:21:05Z)
• Learning to Bound Counterfactual Inference in Structural Causal Models from Observational and Randomised Data [64.96984404868411]
  We derive a likelihood characterisation for the overall data that leads us to extend a previous EM-based algorithm. The new algorithm learns to approximate the (unidentifiability) region of model parameters from such mixed data sources. It delivers interval approximations to counterfactual results, which collapse to points in the identifiable case.
  arXiv  Detail & Related papers  (2022-12-06T12:42:11Z)
• Probabilistic spatial clustering based on the Self Discipline Learning (SDL) model of autonomous learning [1.9322517897534983]
  Unsupervised clustering algorithm can effectively reduce the dimension of high-dimensional unlabeled data. Traditional clustering algorithm needs to set the upper bound of the number of categories in advance. Probability spatial clustering algorithm based on the Self Discipline Learning(SDL) model is proposed.
  arXiv  Detail & Related papers  (2022-01-07T17:18:57Z)
• Exact and Bounded Collision Probability for Motion Planning under Gaussian Uncertainty [1.3535770763481902]
  We present an approach for computing the collision probability under Gaussian distributed motion. The collision condition is formulated as the distance between ellipsoids. We provide a tight upper bound that can be computed much faster during online planning.
  arXiv  Detail & Related papers  (2021-10-12T20:45:18Z)
• Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations [68.0524382279567]
  We present a robust learning algorithm to detect and handle collisions in 3D deforming meshes. Our approach outperforms supervised learning methods and achieves $93.8-98.1%$ accuracy.
  arXiv  Detail & Related papers  (2021-10-08T04:08:31Z)
• A Stochastic Newton Algorithm for Distributed Convex Optimization [62.20732134991661]
  We analyze a Newton algorithm for homogeneous distributed convex optimization, where each machine can calculate gradients of the same population objective. We show that our method can reduce the number, and frequency, of required communication rounds compared to existing methods without hurting performance.
  arXiv  Detail & Related papers  (2021-10-07T17:51:10Z)
• Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles [86.9067793493874]
  We propose efficient mechanisms to characterize and generate testing scenarios using a state-of-the-art driving simulator. We use our method to characterize real driving data from the Next Generation Simulation (NGSIM) project. We rank the scenarios by defining metrics based on the complexity of avoiding accidents and provide insights into how the AV could have minimized the probability of incurring an accident.
  arXiv  Detail & Related papers  (2021-03-12T17:00:23Z)
• DiffCo: Auto-Differentiable Proxy Collision Detection with Multi-class Labels for Safety-Aware Trajectory Optimization [5.35056843248536]
  We present DiffCo, the first, fully auto-differentiable, non-parametric model for collision detection. It provides robust gradients for trajectory optimization via backpropagation and is often 10-100x faster to compute than its geometric counterparts.
  arXiv  Detail & Related papers  (2021-02-15T09:31:26Z)

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.