The Haptic Link Enabling Driver-Automation Teaming

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A vehicle's steering is a particular system in that it is exposed to individual subjective reviews based on criteria that are hard to assess quantitatively. Haptic design of such systems is a prime concern that has been at the center of industrial developm ...

Increasing the capability of automated driving vehicles is motivated by environmental, productivity, and traffic safety benefits. But over-reliance on the automation system is known to cause accidents. The role of the driver cannot be underestimated as it ...

In this thesis, we address the complex issue of collision avoidance in the joint space of robots. Avoiding collisions with both the robot's own body parts and obstacles in the environment is a critical constraint in motion planning and is crucial for ensur ...

The field of artificial intelligence is set to fuel the future of mobility by driving forward the transition from advanced driver-assist systems to fully autonomous vehicles (AV). Yet the current technology, backed by cutting-edge deep learning techniques, ...

Forecasting pedestrians' future motions is essential for autonomous driving systems to safely navigate in urban areas. However, existing prediction algorithms often overly rely on past observed trajectories and tend to fail around abrupt dynamic changes, s ...

Safety is still the main issue of autonomous driving, and in order to be globally deployed, they need to predict pedestrians' motions sufficiently in advance. While there is a lot of research on coarse-grained (human center prediction) and fine-grained pre ...

The ability to predict pedestrian behaviour is crucial for road safety, traffic management systems, Advanced Driver Assistance Systems (ADAS), and more broadly autonomous vehicles. We present a vision-based system that simultaneously locates where pedestri ...

Stereo matching aims to perceive the 3D geometric configuration of scenes and facilitates a variety of computer vision in advanced driver assistance systems (ADAS) applications. Recently, deep convolutional neural networks (CNNs) have shown dramatic perfor ...

In the era of big data, new transportation-related concepts and methodologies need to be proposed to understand how congestion propagates. pNEUMA, a unique dataset that was acquired during a first-of-its-kind experiment using a swarm of drones over a dense ...

Driving is a very challenging task to automatize despite how naturally and efficiently it may come to experienced human drivers. The complexity stems from the need to (i) understand the surrounding context and forecast how it is likely to evolve, (ii) plan ...

In the era of big data, new transportation-related concepts and methodologies need to be proposed for understanding how congestion propagates. pNEUMA, a unique dataset that was acquired during a first-of-its-kind experiment with a swarm of drones over a de ...

A novel manifold learning approach is presented to incorporate computationally efficient obstacle avoidance constraints in optimal control algorithms. The method presented provides a significant computational benefit by reducing the number of constraints r ...

Modern cars can support their drivers by assessing and autonomously performing different driving maneuvers based on information gathered by in-car sensors. We propose that brain–machine interfaces (BMIs) can provide complementary information that can ease ...

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. This is challenging because human motion is inherently multimodal: given a histor ...

Applications of robots for gathering data in underwater environments has been limited due to the challenges posed by the medium. We have developed a miniature, agile, easy to carry and deploy Autonomous Underwater Vehicle (AUV) equipped with a suite of sen ...

The main focus of this research is the development of real-time recording and analysis devices and algorithms for detecting Electroencephalography (EEG) correlates of steering actions. First, offline analysis where performed in a high fidelity driving simu ...

The convergence of sensor-based vehicle automation and Inter-Vehicle Communication (IVC) will be a key to achieve the full automation of vehicles. In this paper we present a new method for the design and performance evaluation of Cooperative Automated Driv ...

The degree of intelligence built-in in today's vehicles in constantly on the rise. The vehicles are being equipped with sensors, with the goal to estimate the state of the vehicle and the environment surrounding it. Intelligent algorithms that process the ...

The intelligent building, a major smart city research and development domain, has grown beyond the scope of automation, currently focusing on the occupant centered approaches and the ancillary services offered to the power grid. The Internet of Things (IoT ...

Traffic problems have mainly resulted from an increase in mobility over the last decades involving an increase in traffic congestion and degradation of environmental conditions. The employment of new systems - namely, Intelligent Transport Systems (ITS) is ...

The Haptic Link Enabling Driver-Automation Teaming

Chattez avec Graph Search

Haptics based collaborative steering control in automated driving environments

Haptics based multi-level collaborative steering control for automated driving

Implicit Distance Functions: Learning and Applications in Robotics

Reshaping Perception for Autonomous Driving with Semantic Keypoints

Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion

Pedestrian 3D Bounding Box Prediction

Pedestrian intention prediction: A convolutional bottom-up multi-task approach

Unsupervised Stereo Matching Using Confidential Correspondence Consistency

Lane Detection and Lane-Changing Identification with High-Resolution Data from a Swarm of Drones

Motion planning for CAVs in mixed traffic, a study on roundabouts

Lane Detection and Lane Changing Phenomena Identification with Data from a Swarm of Drones

Manifold Learning and Optimal Control for Obstacle Avoidance in Autonomous Driving

Decoding Neural Correlates of Cognitive States to Enhance Driving Experience

Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks

Autonomous Feature Tracing and Adaptive Sampling in Real-World Underwater Environments

Detecting EEG correlates during preparation of complex driving maneuvers

Simulation of Cooperative Automated Driving by Bidirectional Coupling of Vehicle and Network Simulators

Cooperative Perception Algorithms for Networked Intelligent Vehicles

Towards the next generation of intelligent building: An assessment study of current automation and future IoT based systems with a proposal for transitional design

Position accuracy with redundant MEMS IMU for road applications