Pedestrian Detection and Tracking for Mobile Robots in Human Environments

With the performance of current motion planning methods being highly dependent on the quality of the perception system, robust 3D multi-object detection and tracking are vital for autonomous driving applications. Despite all the advancements in 2D and 3D object detectors, robust tracking of pedestrians in dense scenarios is still a challenging...

Effect of motion and motivation on task performance, workload and motion sickness

Following the literature review, our goal was to study the effect and interaction of motion sickness and motivation on cognitive performance in a reading comprehension task and the associated workload with the task. We chose UCKAT reading tasks for our cognitive task, monetary incentive and ranks as our motivator and a multisine sickening motion...

Acoustic Perception in Intelligent Vehicles using a single microphone system

Passive acoustic sensing utilizes the ability of sound to travel beyond the line-of-sight to understand the surroundings. This provides an advantage over the currently used sensors in Intelligent Vehicles that can sense obstacles within their line-of-sight only. Recently, a localization based approach has been implemented to take advantage of...

Predicting pedestrian path using optical flow as context cue

A human driver can gauge the intention and signals given by other road users indicative of their future behaviour. The intentions and signals are identified by looking at the cues originating from vulnerable road users or their surroundings (hand signals, head orientation, posture, traffic signals, distance to curb, etc.). Taking all these cues...

3D Object Detection For Intelligent Vehicles

This master thesis presents an experimental study on 3D person localization (i.e., pedestrians, cyclists)in traffic scenes, using monocular vision and Light Detection And Ranging (LiDAR) data. The performance of two top-ranking methods is analyzed on the 3D object detection KITTI dataset. In this evaluation, the effect of the Intersection over...

Traffic Gesture Classification for Intelligent Vehicles

Self-driving vehicles have shown rapid development in recent years and continue to move towards full autonomy. For high or full automation, self-driving vehicles will have to be able to address and solve a broad range of situations, one of which is interaction with traffic agents. For correct and save maneuvering through these situations,...

Curvature Aware Motion Planning with Closed-Loop Rapidly-exploring Random Trees: Towards a generic motion planning algorithm

Last decades the autonomous driving research field has shown exponential growth. The social benefits, which include increased safety, mobility and productivity, are the main factor that drive this growth. One of the most difficult problems that vehicle engineers must solve to develop autonomous vehicles is the motion planning problem. They must...

Evaluating two automation feedback aids’ influence on the acceptance of automated truck merging systems

Merging is one of the most demanding tasks for a truck driver due to the size, weight and limited visual clearance of a truck. Automating the merging procedure can be the solution for a considerable number of accidents by removing the human factor. People have accepted the risk they are in when driving or being driven but acceptance of automated...

Acoustic recognition of motorized vehicles with a moving listener

New measures have to be taken to combat fatalities caused by traffic accidents. Intelligent vehicles have the potential to increase safety, but depend heavily on their automated perception ability.<br/>Acoustic perception, an unused sensing modality in this field, has potential for the detection of nearby vehicles, an ability both human drivers...

Deep End-to-end Network for 3D Object Detection in the Context of Autonomous Driving

Nowadays, autonomous driving is a trending topic in the automotive field. One of the most crucial challenges of autonomous driving research is environment perception. Currently, many techniques achieve satisfactory performance in 2D object detection using camera images. Nevertheless, such 2D object detection might be not sufficient for autonomous...

Depth-aware Instance Segmentation with a Discriminative Loss Function

This work explores the possibility of incorporating depth information into a deep neural network to improve accuracy of RGB instance segmentation. The baseline of this work is semantic instance segmentation with discriminative loss function.The baseline work proposes a novel discriminative loss function with which the semantic net-work can learn...

Longitudinal Behavior Planning with Maneuver Prediction for Urban Driving

One of the remaining challenges in the development of intelligent vehicles is the topic of behavior planning in urban scenarios. Based on perception of the environment around the intelligent vehicle, driving behavior has to be optimized to achieve a comfortable driving experience without sacrificing safety. This work covers the development of a...