Event-Based Obstacle Detection with Commercial Lidar

Computerized obstacle detection for moving vehicles is becoming more important as vehicle manufacturers make their systems more autonomous and safe. However, obstacle detection must operate quickly in dynamic environments such as driving at highway speeds. A unique obstacle detection system using 3D changes in the environment is proposed. Furthermore, these 3D changes are shown to contain sufficient information for avoiding obstacles. To make the system easy to integrate onto a vehicle, additional processing is implemented to remove unnecessary dependencies. This system provides a method for obstacle detection that breaks away from typical systems to be more efficient

Dynamic malware analysis of phishing emails

© 2018 IEEE. Malicious software or malware is one of the most significant dangers facing the Internet today. In the fight against malware, users depend on anti-malware and anti-virus products to proactively detect threats before damage is done. Those products rely on static signatures obtained through malware analysis. Unfortunately, malware authors are always one step ahead in avoiding detection. This research deals with dynamic malware analysis, which emphasizes on: how the malware will behave after execution, what changes to the operating system, registry and network communication take place. Dynamic analysis opens up the doors for automatic generation of anomaly and active signatures based on the new malware\u27s behavior. The research includes a design of honeypot to capture new malware and a complete dynamic analysis laboratory setting. We propose a standard analysis methodology by preparing the analysis tools, then running the malicious samples in a controlled environment to investigate their behavior. We analyze 173 recent Phishing emails and 45 SPIM messages in search for potentially new malwares, we present two malware samples and their comprehensive dynamic analysis

Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions

To plan safe trajectories in urban environments, autonomous vehicles must be able to quickly assess the future intentions of dynamic agents. Pedestrians are particularly challenging to model, as their motion patterns are often uncertain and/or unknown a priori. This paper presents a novel changepoint detection and clustering algorithm that, when coupled with offline unsupervised learning of a Gaussian process mixture model (DPGP), enables quick detection of changes in intent and online learning of motion patterns not seen in prior training data. The resulting long-term movement predictions demonstrate improved accuracy relative to offline learning alone, in terms of both intent and trajectory prediction. By embedding these predictions within a chance-constrained motion planner, trajectories which are probabilistically safe to pedestrian motions can be identified in real-time. Hardware experiments demonstrate that this approach can accurately predict pedestrian motion patterns from onboard sensor/perception data and facilitate robust navigation within a dynamic environment.Comment: Submitted to 2014 International Workshop on the Algorithmic Foundations of Robotic

A macroscopic analytical model of collaboration in distributed robotic systems

In this article, we present a macroscopic analytical model of collaboration in a group of reactive robots. The model consists of a series of coupled differential equations that describe the dynamics of group behavior. After presenting the general model, we analyze in detail a case study of collaboration, the stick-pulling experiment, studied experimentally and in simulation by Ijspeert et al. [Autonomous Robots, 11, 149-171]. The robots' task is to pull sticks out of their holes, and it can be successfully achieved only through the collaboration of two robots. There is no explicit communication or coordination between the robots. Unlike microscopic simulations (sensor-based or using a probabilistic numerical model), in which computational time scales with the robot group size, the macroscopic model is computationally efficient, because its solutions are independent of robot group size. Analysis reproduces several qualitative conclusions of Ijspeert et al.: namely, the different dynamical regimes for different values of the ratio of robots to sticks, the existence of optimal control parameters that maximize system performance as a function of group size, and the transition from superlinear to sublinear performance as the number of robots is increased