arXiv research

DeepLocalization uses neural networks to localize vehicles from landmarks.

We apply variational inference to learn vehicle trajectory parameters from noisy data.

Improved UAV navigation and landing using deep learning.

Proposes a method to model multi-vehicle interactions using Gaussian processes.

This paper tackles security challenges in CAVs using ML, proposing defenses against adversarial attacks.

DFKI Cabin Simulator tests visual monitoring functions in vehicles.

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…

mm-Pose detects human skeletons in real-time using mmWave radar and CNNs.

Optimal vehicle repositioning policy found for shared mobility services.

A structured approach to generating adversarial attacks for ML systems.

Deep learning predicts V2I channel responses for high-mobility wireless communications.

Deep learning has revolutionized the ability to learn "end-to-end" autonomous vehicle control directly from raw sensory data. While there have been recent extensions to handle forms of navigation instruction, these works are unable to capture the full distribution of possible actions that could be taken and to reason a…

Deep learning tool classifies urban delivery vehicles.

Adaptive framework generates challenging adversarial scenarios for autonomous vehicles.

VTrackIt creates a synthetic dataset with infrastructure and vehicle info for AVs.

Reinforcement learning is considered to be a strong AI paradigm which can be used to teach machines through interaction with the environment and learning from their mistakes. Despite its perceived utility, it has not yet been successfully applied in automotive applications. Motivated by the successful demonstrations of…

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…

Proposes a privacy-preserving system for federated learning of road networks.

Generative model predicts vehicle faults up to 1000 hours in advance.

Generative model learns vehicle trajectory distributions for better data generalization.

Convolutional neural networks are the most widely used deep learning algorithms for traffic signal classification till date but they fail to capture pose, view, orientation of the images because of the intrinsic inability of max pooling layer.This paper proposes a novel method for Traffic sign detection using deep lear…

Proposes CTSDG model for better vehicle intention prediction across domains.

In a mixed-traffic scenario where both autonomous vehicles and human-driving vehicles exist, a timely prediction of driving intentions of nearby human-driving vehicles is essential for the safe and efficient driving of an autonomous vehicle. In this paper, a driving intention prediction method based on Hidden Markov Mo…

PaRoT simplifies robust training for deep neural networks.

The paper reviews machine learning safety techniques for autonomous vehicles.

Bayesian model assesses criminal threat escalation.

New approach uses dynamic programming to efficiently discover failures in autonomous vehicle simulations.

Paper proposes a method to improve autonomous vehicle performance using synthetically generated images.

Autonomous Vehicles(AV) are one of the brightest promises of the future which would help cut down fatalities and improve travel time while working in harmony. Autonomous vehicles will face with challenging situations and experiences not seen before. These experiences should be converted to knowledge and help the vehicl…

This paper explores the capability of deep neural networks to capture key characteristics of vehicle dynamics, and their ability to perform coupled longitudinal and lateral control of a vehicle. To this extent, two different artificial neural networks are trained to compute vehicle controls corresponding to a reference…

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…

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…

Improved AST method finds more useful failure scenarios for autonomous vehicles.

A novel observer-based method detects and recovers anomalies in CAV sensor readings.

Predict real-time crash risks during hurricane evacuations using connected vehicle data.

This paper compares machine learning methods for recognizing lane change intentions from vehicle trajectories.

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

An active area of research is to increase the safety of self-driving vehicles. Although safety cannot be guarenteed completely, the capability of a vehicle to predict the future trajectories of its surrounding vehicles could help ensure this notion of safety to a greater deal. We cast the trajectory forecast problem in…

Deep learning predicts vehicle behavior for safer autonomous driving.

This work improves vehicle trajectory prediction for safer self-driving cars.

Deep learning improves vehicle control performance and generalizes well.

A DRL-based strategy improves vehicle tracking accuracy while saving energy.

Neural LNS improves vehicle routing performance.

Deep Q-learning optimizes same-day delivery with vehicles and drones.

Accurately predicting the possible behaviors of traffic participants is an essential capability for future autonomous vehicles. The majority of current researches fix the number of driving intentions by considering only a specific scenario. However, distinct driving environments usually contain various possible driving…

New approach estimates vehicle and component prices without teardowns.

New machine learning pipeline solves dynamic vehicle routing problems efficiently.

Vehicle recognition and classification have broad applications, ranging from traffic flow management to military target identification. We demonstrate an unsupervised method for automated identification of moving vehicles from roadside audio sensors. Using a short-time Fourier transform to decompose audio signals, we t…