Related papers: GLADAS: Gesture Learning for Advanced Driver Assis…
For a foreseeable future, autonomous vehicles (AVs) will operate in traffic together with human-driven vehicles. Their planning and control systems need extensive testing, including early-stage testing in simulations where the interactions…
Human action recognition (HAR) in videos is a fundamental research topic in computer vision. It consists mainly in understanding actions performed by humans based on a sequence of visual observations. In recent years, HAR have witnessed…
In this paper we present an original contactless human machine interface for driving car. The proposed framework is based on the image sent by a simple camera device, which is then processed by various computer vision algorithms. These…
Vulnerable road users (VRUs) face high collision risks in mixed traffic, yet most existing safety systems prioritize driver or vehicle assistance over direct VRU support. This paper presents ARCAS, a real-time augmented reality (AR)…
Recent success suggests that deep neural control networks are likely to be a key component of self-driving vehicles. These networks are trained on large datasets to imitate human actions, but they lack semantic understanding of image…
We present a joint camera and radar approach to enable autonomous vehicles to understand and react to human gestures in everyday traffic. Initially, we process the radar data with a PointNet followed by a spatio-temporal multilayer…
Highway merging scenarios featuring mixed traffic conditions pose significant modeling and control challenges for connected and automated vehicles (CAVs) interacting with incoming on-ramp human-driven vehicles (HDVs). In this paper, we…
This paper develops a novel car-following control method to reduce voluntary driver interventions and improve traffic stability in Automated Vehicles (AVs). Through a combination of experimental and empirical analysis, we show how voluntary…
Advanced Driver Assistance Systems (ADAS) have made driving safer over the last decade. They prepare vehicles for unsafe road conditions and alert drivers if they perform a dangerous maneuver. However, many accidents are unavoidable because…
Automated driving is one of the most active research areas in computer science. Deep learning methods have made remarkable breakthroughs in machine learning in general and in automated driving (AD)in particular. However, there are still…
In the fast-paced field of human-computer interaction (HCI) and virtual reality (VR), automatic gesture recognition has become increasingly essential. This is particularly true for the recognition of hand signs, providing an intuitive way…
Interactions between vehicles and pedestrians have always been a major problem in traffic safety. Experienced human drivers are able to analyze the environment and choose driving strategies that will help them avoid crashes. What is not yet…
{Recognizing human interactions is essential for social robots as it enables them to navigate safely and naturally in shared environments. Conventional robotic systems however often focus on obstacle avoidance, neglecting social cues…
The autonomous driving (AD) industry is exploring the use of knowledge graphs (KGs) to manage the vast amount of heterogeneous data generated from vehicular sensors. The various types of equipped sensors include video, LIDAR and RADAR.…
By sharing information across multiple agents, collaborative perception helps autonomous vehicles mitigate occlusions and improve overall perception accuracy. While most previous work focus on vehicle-to-vehicle and…
We present advancements in the design and development of in-vehicle infotainment systems that utilize gesture input and ultrasonic mid-air haptic feedback. Such systems employ state-of-the-art hand tracking technology and novel haptic…
Automated Driving Systems (ADSs) are being manufactured at an accelerated rate, leading to improvements in traffic safety, reduced energy consumption, pollution, and congestion. ADS relies on various data streams from onboard sensors,…
Rapid advancements in driver-assistance technology will lead to the integration of fully autonomous vehicles on our roads that will interact with other road users. To address the problem that driverless vehicles make interaction through eye…
Existing Autonomous Driving Systems (ADS) independently make driving decisions, but they face two significant limitations. First, in complex scenarios, ADS may misinterpret the environment and make inappropriate driving decisions. Second,…
Recent advances in AI and intelligent vehicle technology hold promise to revolutionize mobility and transportation, in the form of advanced driving assistance (ADAS) interfaces. Although it is widely recognized that certain cognitive…