arXiv research

Model forecasts motor vehicle collision rates with high accuracy.

This paper concerns automated vehicles negotiating with other vehicles, typically human driven, in crossings with the goal to find a decision algorithm by learning typical behaviors of other vehicles. The vehicle observes distance and speed of vehicles on the intersecting road and use a policy that adapts its speed alo…

An important application of intelligent vehicles is advance detection of dangerous events such as collisions. This problem is framed as a problem of optimal alarm choice given predictive models for vehicle location and motion. Techniques for real-time collision detection are surveyed and grouped into three classes: ran…

CARML uses meta-learning to avoid obstacles in 2D vehicle navigation.

This paper presents a method for testing the decision making systems of autonomous vehicles. Our approach involves perturbing stochastic elements in the vehicle's environment until the vehicle is involved in a collision. Instead of applying direct Monte Carlo sampling to find collision scenarios, we formulate the probl…

Road accidents are an important issue of our modern societies, responsible for millions of deaths and injuries every year in the world. In Quebec only, in 2018, road accidents are responsible for 359 deaths and 33 thousands of injuries. In this paper, we show how one can leverage open datasets of a city like Montreal, …

CriticSMC improves planning efficiency in constrained environments.

To improve efficiency and reduce failures in autonomous vehicles, research has focused on developing robust and safe learning methods that take into account disturbances in the environment. Existing literature in robust reinforcement learning poses the learning problem as a two player game between the autonomous system…

Deep RL mimics human driving for collision avoidance in self-driving cars.

Study shows insurance industry in North Macedonia declined 10% due to COVID-19.

Collision avoidance is a critical task in many applications, such as ADAS (advanced driver-assistance systems), industrial automation and robotics. In an industrial automation setting, certain areas should be off limits to an automated vehicle for protection of people and high-valued assets. These areas can be quaranti…

Researchers develop PAIN to improve self-driving safety through adversarial training.

Deep reinforcement learning provides a promising approach for vision-based control of real-world robots. However, the generalization of such models depends critically on the quantity and variety of data available for training. This data can be difficult to obtain for some types of robotic systems, such as fragile, smal…

DeepHybrid uses radar data to classify objects accurately.

A new metric for uncertainty quantification using class collisions.

DiffSlack learns neural networks with nonlinear constraints via learnable slack variables.

Situational awareness in vehicular networks could be substantially improved utilizing reliable trajectory prediction methods. More precise situational awareness, in turn, results in notably better performance of critical safety applications, such as Forward Collision Warning (FCW), as well as comfort applications like …

CitySim dataset captures vehicle trajectories for safety research.

Counterfactual policy evaluation improves autonomous driving policies' generalization.

In order to maximize detection precision rate as well as the recall rate, this paper proposes an in-vehicle multi-source fusion scheme in Keyword Spotting (KWS) System for vehicle applications. Vehicle information, as a new source for the original system, is collected by an in-vehicle data acquisition platform while th…

New algorithm for multi-player bandits with collision-dependent rewards.

Study improves queue length estimation from connected vehicles by filtering parameters.

Develops a new model for controllable and realistic traffic simulation.

A model used for velocity control during car following was proposed based on deep reinforcement learning (RL). To fulfil the multi-objectives of car following, a reward function reflecting driving safety, efficiency, and comfort was constructed. With the reward function, the RL agent learns to control vehicle speed in …

Study improves crash rate forecasting in Washington, D.C. using stochastic volatility model.

Study on implied certainty equivalent rates in financial markets and electric vehicles.

We consider the non-stochastic version of the (cooperative) multi-player multi-armed bandit problem. The model assumes no communication at all between the players, and furthermore when two (or more) players select the same action this results in a maximal loss. We prove the first $\sqrt{T}$-type regret guarantee for th…

Study shows how transformers classify symbols without naming them, proving a margin-versus-collision criterion.

Machine learning models adapt to motor learning but face challenges.

This paper investigates the autonomous control of massive unmanned aerial vehicles (UAVs) for mission-critical applications (e.g., dispatching many UAVs from a source to a destination for firefighting). Achieving their fast travel and low motion energy without inter-UAV collision under wind perturbation is a daunting c…

Deep neural features identify unique vehicles from dash-cam feeds.

Residual neural networks improve collision prediction in planetary simulations.

A tree-based IDS detects cyber-attacks in AV networks.

Study nonholonomic systems with collisions using variational principles.

End-to-end CNN for real-time MOD improves KITTI dataset accuracy by 8%.

Paper analyzes dynamics of nonholonomic systems with collisions using variational techniques.

Machine learning competition predicts spacecraft collision risks.

New algorithms estimate and test collision probability with near-optimal sample complexity.

One approach to designing decision making logic for an aircraft collision avoidance system frames the problem as a Markov decision process and optimizes the system using dynamic programming. The resulting collision avoidance strategy can be represented as a numeric table. This methodology has been used in the developme…

No-collision maps improve manifold learning for image data.

BAMS uses Bayesian sampling to discover AV failures more efficiently and accurately.

Reduces necessary conditions for collision avoidance on curved spaces.

This work optimizes signal estimation for sparse MRA with collision-free signals.

Study aims to measure and mitigate biases in motor insurance pricing.

The paper addresses the Multiplayer Multi-Armed Bandit (MMAB) problem, where $M$ decision makers or players collaborate to maximize their cumulative reward. When several players select the same arm, a collision occurs and no reward is collected on this arm. Players involved in a collision are informed about this collis…

Algorithm reduces regret in multi-player bandits with unknown collision rewards.

A new algorithm reduces regret in multi-player bandits without collision info.

A new algorithm RESYNC for defenders against malicious attackers in multi-player bandits.