Collision Avoidance

87 papers with code • 0 benchmarks • 0 datasets

This task has no description! Would you like to contribute one?


Use these libraries to find Collision Avoidance models and implementations
2 papers
2 papers

Most implemented papers

Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks

guykatzz/ReluplexCav2017 3 Feb 2017

Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems.

Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks

agrimgupta92/sgan CVPR 2018

Understanding human motion behavior is critical for autonomous moving platforms (like self-driving cars and social robots) if they are to navigate human-centric environments.

Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning

mfe7/cadrl_ros 4 May 2018

This work extends our previous approach to develop an algorithm that learns collision avoidance among a variety of types of dynamic agents without assuming they follow any particular behavior rules.

Formal Security Analysis of Neural Networks using Symbolic Intervals

tcwangshiqi-columbia/ReluVal 28 Apr 2018

In this paper, we present a new direction for formally checking security properties of DNNs without using SMT solvers.

Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learning

mit-acl/gym-collision-avoidance 24 Oct 2019

Collision avoidance algorithms are essential for safe and efficient robot operation among pedestrians.

Learning Sampling Distributions for Robot Motion Planning

StanfordASL/LearnedSamplingDistributions 16 Sep 2017

This paper proposes a methodology for non-uniform sampling, whereby a sampling distribution is learned from demonstrations, and then used to bias sampling.

Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning

Acmece/rl-collision-avoidance 28 Sep 2017

We validate the learned sensor-level collision avoidance policy in a variety of simulated scenarios with thorough performance evaluations and show that the final learned policy is able to find time efficient, collision-free paths for a large-scale robot system.

Efficient Formal Safety Analysis of Neural Networks

tcwangshiqi-columbia/ReluVal NeurIPS 2018

Our approach can check different safety properties and find concrete counterexamples for networks that are 10$\times$ larger than the ones supported by existing analysis techniques.

Provable Repair of Deep Neural Networks

95616ARG/PRDNN 9 Apr 2021

This has motivated a large number of techniques for finding unsafe behavior in DNNs.

MODS -- A USV-oriented object detection and obstacle segmentation benchmark

lojzezust/WaSR 5 May 2021

We propose a new obstacle segmentation performance evaluation protocol that reflects the detection accuracy in a way meaningful for practical USV navigation.