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The process of developing control functions for embedded systems is resource-, time-, and data-intensive, often resulting in sub-optimal cost and solutions approaches. Reinforcement Learning (RL) has great potential for autonomously…
Autonomous car racing is a challenging task in the robotic control area. Traditional modular methods require accurate mapping, localization and planning, which makes them computationally inefficient and sensitive to environmental changes.…
Simulations are gaining increasingly significance in the field of autonomous driving due to the demand for rapid prototyping and extensive testing. Employing physics-based simulation brings several benefits at an affordable cost, while…
Human-driven vehicles (HVs) amplify naturally occurring perturbations in traffic, leading to congestion--a major contributor to increased fuel consumption, higher collision risks, and reduced road capacity utilization. While previous…
Simulation is essential to validate autonomous driving systems. However, a simple simulation, even for an extremely high number of simulated miles or hours, is not sufficient. We need well-founded criteria showing that simulation does…
Autonomous vehicles are one of the most popular and also fast-growing technologies in the world. As we go further, there are still a lot of challenges that are unsolved and may cause problems in the future when it comes to testing in real…
The increasing use of robots in unstructured environments necessitates the development of effective perception and navigation strategies to enable field robots to successfully perform their tasks. In particular, it is key for such robots to…
Lane change decision-making for autonomous vehicles is a complex but high-reward behavior. In this paper, we propose a hybrid input based deep reinforcement learning (DRL) algorithm, which realizes abstract lane change decisions and lane…
As semi-automated vehicles (SAVs) become more common, ensuring effective human-vehicle interaction during control handovers remains a critical safety challenge. Existing studies often rely on single-session simulator experiments or…
Simulators are an important tool in robotics that is used to develop robot software and generate synthetic data for machine learning algorithms. Faster simulation can result in better software validation and larger amounts of data. Previous…
Enhancing simulation environments to replicate real-world driver behavior is essential for developing Autonomous Vehicle technology. While some previous works have studied the yielding reaction of lag vehicles in response to a merging car…
Automotive software testing continues to rely largely upon expensive field tests to ensure quality because alternatives like simulation-based testing are relatively immature. As a step towards lowering reliance on field tests, we present…
In this paper, we propose a real-time adaptive prediction method to calculate smooth and accurate haptic feedback in complex scenarios. Smooth haptic feedback is an important task for haptic rendering with complex virtual objects. However,…
An appropriate reward function is of paramount importance in specifying a task in reinforcement learning (RL). Yet, it is known to be extremely challenging in practice to design a correct reward function for even simple tasks.…
Simulation testing is a fundamental approach for evaluating automated vehicles (AVs). To ensure its reliability, it is crucial to accurately replicate interactions between AVs and background traffic, which necessitates effective…
Virtual Reality (VR) is seeing increased adoption across many fields. The field of experimental cognitive science is also testing utilization of the technology combined with physiological measures such as electroencephalography (EEG) and…
This paper proposes a novel concept of a hybrid tactile display with multistimulus feedback, allowing the real-time experience of the position, shape, and texture of the virtual object. The key technology of the TeslaMirror is that we can…
This paper presents the development of a real-time simulator for the validation of controlling a large vehicle manipulator. The need for this development can be justified by the lack of such a simulator: There are neither open source…
The increasing integration of renewable energy into the power grid has highlighted the critical importance of demand-side flexibility. Among flexible loads, heating, ventilation, and air-conditioning (HVAC) systems are particularly…
Recently, we developed a dynamic distributed end-to-end vehicle routing system (E2ECAV) using a network of intelligent intersections and level 5 CAVs (Djavadian & Farooq, 2018). The case study of the downtown Toronto Network showed that…