Stable Dynamic Predictive Clustering (SDPC) Protocol for Vehicular Ad hoc Network

Vehicular communication is an essential part of a smart city. Scalability is a major issue for vehicular communication, specially, when the number of vehicles increases at any given point. Vehicles also suffer some other problems such as broadcast problem. Clustering can solve the issues of vehicular ad hoc network (VANET); however, due to the high mobility of the vehicles, clustering in VANET suffers stability issue. Previously proposed clustering algorithms for VANET are optimized for either straight road or for intersection. Moreover, the absence of the intelligent use of a combination of the mobility parameters, such as direction, movement, position, velocity, degree of vehicle, movement at the intersection etc., results in cluster stability issues. A dynamic clustering algorithm considering the efficient use of all the mobility parameters can solve the stability problem in VANET. To achieve higher stability for VANET, a novel robust and dynamic clustering algorithm stable dynamic predictive clustering (SDPC) for VANET is proposed in this paper. In contrast to previous studies, vehicle relative velocity, vehicle position, vehicle distance, transmission range, and vehicle density are considered in the creation of a cluster, whereas relative distance, movement at the intersection, degree of vehicles are considered to select the cluster head. From the mobility parameters the future road scenario is constructed. The cluster is created, and the cluster head is selected based on the future construction of the road. The performance of SDPC is compared in terms of the average cluster head change rate, the average cluster head duration, the average cluster member duration, and the ratio of clustering overhead in terms of total packet transmission. The simulation result shows SDPC outperforms the existing algorithms and achieved better clustering stability

Enhanced free space detection in multiple lanes based on single CNN with scene identification

Many systems for autonomous vehicles' navigation rely on lane detection. Traditional algorithms usually estimate only the position of the lanes on the road, but an autonomous control system may also need to know if a lane marking can be crossed or not, and what portion of space inside the lane is free from obstacles, to make safer control decisions. On the other hand, free space detection algorithms only detect navigable areas, without information about lanes. State-of-the-art algorithms use CNNs for both tasks, with significant consumption of computing resources. We propose a novel approach that estimates the free space inside each lane, with a single CNN. Additionally, adding only a small requirement concerning GPU RAM, we infer the road type, that will be useful for path planning. To achieve this result, we train a multi-task CNN. Then, we further elaborate the output of the network, to extract polygons that can be effectively used in navigation control. Finally, we provide a computationally efficient implementation, based on ROS, that can be executed in real time. Our code and trained models are available online.Comment: Will appear in the 2019 IEEE Intelligent Vehicles Symposium (IV 2019

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

Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning

Providing an efficient strategy to navigate safely through unsignaled intersections is a difficult task that requires determining the intent of other drivers. We explore the effectiveness of Deep Reinforcement Learning to handle intersection problems. Using recent advances in Deep RL, we are able to learn policies that surpass the performance of a commonly-used heuristic approach in several metrics including task completion time and goal success rate and have limited ability to generalize. We then explore a system's ability to learn active sensing behaviors to enable navigating safely in the case of occlusions. Our analysis, provides insight into the intersection handling problem, the solutions learned by the network point out several shortcomings of current rule-based methods, and the failures of our current deep reinforcement learning system point to future research directions.Comment: IEEE International Conference on Robotics and Automation (ICRA 2018

Modeling the Internet of Things: a simulation perspective

This paper deals with the problem of properly simulating the Internet of Things (IoT). Simulating an IoT allows evaluating strategies that can be employed to deploy smart services over different kinds of territories. However, the heterogeneity of scenarios seriously complicates this task. This imposes the use of sophisticated modeling and simulation techniques. We discuss novel approaches for the provision of scalable simulation scenarios, that enable the real-time execution of massively populated IoT environments. Attention is given to novel hybrid and multi-level simulation techniques that, when combined with agent-based, adaptive Parallel and Distributed Simulation (PADS) approaches, can provide means to perform highly detailed simulations on demand. To support this claim, we detail a use case concerned with the simulation of vehicular transportation systems.Comment: Proceedings of the IEEE 2017 International Conference on High Performance Computing and Simulation (HPCS 2017