Vinu kamalasanan

Bio: Vinu kamalasanan is an academic researcher from Leibniz University of Hanover. The author has contributed to research in topics: Computer science & Augmented reality. The author has an hindex of 1, co-authored 3 publications receiving 5 citations.

Behaviour Control with Augmented Reality Systems for Shared Spaces

Abstract: . Augmented Reality (AR) in a traffic context has mainly been used in navigation with path augmentation, focused around safely guiding the user with prior knowledge of the route and the destination. Other works are reported to warn drivers by visualizing other traffic participants or dangers, which are yet currently out of sight. However they do not cover aspects of mediating control by recommending users with actions, even when such efforts are expected to foster collaboration in a multiagent environment. To the best of our knowledge, AR has not yet been applied to visualize virtual control information, e.g. virtual lanes or signposts, notably in the context of shared spaces. Such an environment should support spatial understanding of proximate participants with adaptive augmented controls to recommend actions to each user. However when such systems work in context where a conflict of interest would arise, a rule based control logic centered on priority should be accounted for. Traditionally, these rules are defined by traffic management. This paper presents a Behaviour Control with AR (BCAR) Systems based framework for control of user behaviour in a shared space via augmentation and proposes how a control logic can be part of it. The framework which incorporates navigation focuses on mapping users from real to the virtual world .This framework also enables simulations and visualization of multiagent interactions and proposing controls for user actions leveraging the environment complexity reduction achieved through the real to virtual transfer. A prototype implementation of the proposed framework with ARCore and unity3D has been evaluated for pedestrian behaviour control to understand its feasibility.

Exploratory Study on the Use of Augmentation for Behavioural Control in Shared Spaces

Abstract: Shared spaces are regulation free, mixed traffic environments supporting social interactions between pedestrian, cyclist and vehicles. Even when these spaces are designed to foster safety supported by reduced traffic speeds, unforeseen collisions and priority conflicts are always an open question. While AR can be used to realise virtual pedestrian lanes and traffic signals, the change in pedestrian motion dynamics using such approaches needs to be understood. This work highlights an exploratory study to evaluate how speed and path of pedestrians are impacted when using an augmented reality based virtual traffic light interface to control collisions in pedestrian motion. To achieve this objective we analyse the motion information from controlled experiments, replicating pedestrian motion on a lane supported by a stop and go interface and including scenarios such as confronting a crossing pedestrian. Our statistical and quantitative analysis gives some early insights on pedestrian control using body worn AR systems

Improving 3d pedestrian detection for wearable sensor data with 2d human pose

Abstract: Abstract. Collisions and safety are important concepts when dealing with urban designs like shared spaces. As pedestrians (especially the elderly and disabled people) are more vulnerable to accidents, realising an intelligent mobility aid to avoid collisions is a direction of research that could improve safety using a wearable device. Also, with the improvements in technologies for visualisation and their capabilities to render 3D virtual content, AR devices could be used to realise virtual infrastructure and virtual traffic systems. Such devices (e.g., Hololens) scan the environment using stereo and ToF (Time-of-Flight) sensors, which in principle can be used to detect surrounding objects, including dynamic agents such as pedestrians. This can be used as basis to predict collisions. To envision an AR device as a safety aid and demonstrate its 3D object detection capability (in particular: pedestrian detection), we propose an improvement to the 3D object detection framework Frustum Pointnet with human pose and apply it on the data from an AR device. Using the data from such a device in an indoor setting, we conducted a comparative study to investigate how high level 2D human pose features in our approach could help to improve the detection performance of orientated 3D pedestrian instances over Frustum Pointnet.

Mixed Reality Agent-Based Framework for Pedestrian-Cyclist Interaction

Abstract: Participating in urban traffic is inherently risky for humans. There-fore, in psychology, behavioural studies have been using Virtual Reality (VR) to simulate and experiment with human behaviour. Safety critical interactions (e.g. conflict, collision or near collision) can be captured from the motion trajectories. However, the motion data in virtual settings is influenced by the modelling software used to create the virtual world, which might fail to capture one-to-one interactions accurately (such as interactions between pedestrians and cyclists in mixed traffic). Our system paper proposes a Pedestrian-in-the-Loop (PIL) Mixed Reality (MR) framework, where mobile virtual cyclist avatars co-exist with humans in a real-world outdoor space. Such a setting can be used to study a pedestrian subject, both viewing and interacting with moving holograms of cyclists in real traffic. The novelty of our approach is modelling virtual avatars as cognitive agents. To achieve this, we integrate agent-based models so that the virtual avatar can sense the environment and interact with the real user participating in the experiments. We demonstrate that this approach could contribute to effectively studying of pedestrian interactions. We also perform an evaluation to quantify the amount of trajectory error for our outdoor framework. For this, we compare the position data of a subject during an experiment to a proven benchmark for indoor motion capture. Additionally, an application of using the framework to demonstrate pedestrian dominance is presented.

Improving Pedestrian Priority via Grouping and Virtual Lanes

Abstract: The shared space design is applied in urban streets to support barrier-free movement and integrate traffic participants (such as pedestrians, cyclists and vehicles) into a common road space. Regardless of the low-speed environment, sharing space with motor vehicles can make vulnerable road users feel uneasy. Yet, walking in groups increases their confidence as well as influence the yielding behavior of drivers. Therefore, we propose an innovative approach to support the crossing of pedestrians via grouping and project the virtual lanes in shared spaces. This paper presents the important components of the crowd steering system, discusses the enablers and gaps in the current approach, and illustrates the proposed idea with concept diagrams.

Vision-only autonomous navigation using topometric maps

Abstract: This paper presents a mapping and navigation system for a mobile robot, which uses vision as its sole sensor modality. The system enables the robot to navigate autonomously, plan paths and avoid obstacles using a vision based topometric map of its environment. The map consists of a globally-consistent pose-graph with a local 3D point cloud attached to each of its nodes. These point clouds are used for direction independent loop closure and to dynamically generate 2D metric maps for locally optimal path planning. Using this locally semi-continuous metric space, the robot performs shortest path planning instead of following the nodes of the graph --- as is done with most other vision-only navigation approaches. The system exploits the local accuracy of visual odometry in creating local metric maps, and uses pose graph SLAM, visual appearance-based place recognition and point clouds registration to create the topometric map. The ability of the framework to sustain vision-only navigation is validated experimentally, and the system is provided as open-source software.

Autonomous Vehicles Drive into Shared Spaces: eHMI Design Concept Focusing on Vulnerable Road Users

Abstract: In comparison to conventional traffic designs, shared spaces promote a more pleasant urban environment with slower motorized movement, smoother traffic, and less congestion. In the foreseeable future, shared spaces will be populated with a mixture of autonomous vehicles (AVs) and vulnerable road users (VRUs) like pedestrians and cyclists. However, a driver-less AV lacks a way to communicate with the VRUs when they have to reach an agreement of a negotiation, which brings new challenges to the safety and smoothness of the traffic. To find a feasible solution to integrating AVs seamlessly into shared-space traffic, we first identified the possible issues that the shared-space designs have not considered for the role of AVs. Then an online questionnaire was used to ask participants about how they would like a driver of the manually driving vehicle to communicate with VRUs in a shared space. We found that when the driver wanted to give some suggestions to the VRUs in a negotiation, participants thought that the communications via the driver's body behaviors were necessary. Besides, when the driver conveyed information about her/his intentions and cautions to the VRUs, participants selected different communication methods with respect to their transport modes (as a driver, pedestrian, or cyclist). These results suggest that novel eHMIs might be useful for AV-VRU communication when the original drivers are not present. Hence, a potential eHMI design concept was proposed for different VRUs to meet their various expectations. In the end, we further discussed the effects of the eHMIs on improving the sociality in shared spaces and the autonomous driving systems.

Towards future pedestrian-vehicle interactions: Introducing theoretically-supported AR prototypes

Abstract: The future urban environment may consist of mixed traffic in which pedestrians interact with automated vehicles (AVs). However, it is still unclear how AVs should communicate their intentions to pedestrians. Augmented reality (AR) technology could transform the future of interactions between pedestrians and AVs by offering targeted and individualized communication. This paper presents nine prototypes of AR concepts for pedestrian-AV interaction that are implemented and demonstrated in a real crossing environment. Each concept was based on expert perspectives and designed using theoretically-informed brainstorming sessions. Prototypes were implemented in Unity MARS and subsequently tested on an unmarked road using a standalone iPad Pro with LiDAR functionality. Despite the limitations of the technology, this paper offers an indication of how future AR systems may support future pedestrian-AV interactions.

Exploratory Study on the Use of Augmentation for Behavioural Control in Shared Spaces

Abstract: Shared spaces are regulation free, mixed traffic environments supporting social interactions between pedestrian, cyclist and vehicles. Even when these spaces are designed to foster safety supported by reduced traffic speeds, unforeseen collisions and priority conflicts are always an open question. While AR can be used to realise virtual pedestrian lanes and traffic signals, the change in pedestrian motion dynamics using such approaches needs to be understood. This work highlights an exploratory study to evaluate how speed and path of pedestrians are impacted when using an augmented reality based virtual traffic light interface to control collisions in pedestrian motion. To achieve this objective we analyse the motion information from controlled experiments, replicating pedestrian motion on a lane supported by a stop and go interface and including scenarios such as confronting a crossing pedestrian. Our statistical and quantitative analysis gives some early insights on pedestrian control using body worn AR systems

Mixed Reality Agent-Based Framework for Pedestrian-Cyclist Interaction

Abstract: Participating in urban traffic is inherently risky for humans. There-fore, in psychology, behavioural studies have been using Virtual Reality (VR) to simulate and experiment with human behaviour. Safety critical interactions (e.g. conflict, collision or near collision) can be captured from the motion trajectories. However, the motion data in virtual settings is influenced by the modelling software used to create the virtual world, which might fail to capture one-to-one interactions accurately (such as interactions between pedestrians and cyclists in mixed traffic). Our system paper proposes a Pedestrian-in-the-Loop (PIL) Mixed Reality (MR) framework, where mobile virtual cyclist avatars co-exist with humans in a real-world outdoor space. Such a setting can be used to study a pedestrian subject, both viewing and interacting with moving holograms of cyclists in real traffic. The novelty of our approach is modelling virtual avatars as cognitive agents. To achieve this, we integrate agent-based models so that the virtual avatar can sense the environment and interact with the real user participating in the experiments. We demonstrate that this approach could contribute to effectively studying of pedestrian interactions. We also perform an evaluation to quantify the amount of trajectory error for our outdoor framework. For this, we compare the position data of a subject during an experiment to a proven benchmark for indoor motion capture. Additionally, an application of using the framework to demonstrate pedestrian dominance is presented.