Stability and Equilibrium Analysis of Laneless Traffic with Local Control Laws

In this paper, a new model for traffic on roads with multiple lanes is developed, where the vehicles do not adhere to a lane discipline. Assuming identical vehicles, the dynamics is split along two independent directions: the Y-axis representing the direction of motion and the X-axis representing the lateral or the direction perpendicular to the direction of motion. Different influence graphs are used to model the interaction between the vehicles in these two directions. The instantaneous accelerations of each car, in both X and Y directions, are functions of the measurements from the neighbouring cars according to these influence graphs. The stability and equilibrium spacings of the car formation is analyzed for usual traffic situations such as steady flow, obstacles, lane changing and rogue drivers arbitrarily changing positions inside the formation. Conditions are derived under which the formation maintains stability and the desired intercar spacing for each of these traffic events. Simulations for some of these scenarios are included.Comment: 8 page

Cognitive Vehicle Platooning in the Era of Automated Electric Transportation

Vehicle platooning is an important innovation in the automotive industry that aims at improving safety, mileage, efficiency, and the time needed to travel. This research focuses on the various aspects of vehicle platooning, one of the important aspects being analysis of different control strategies that lead to a stable and robust platoon. Safety of passengers being a very important consideration, the control design should be such that the controller remains robust under uncertain environments. As a part of the Department of Energy (DOE) project, this research also tries to show a demonstration of vehicle platooning using robots. In an automated highway scenario, a vehicle platoon can be thought of as a string of vehicles, following one another as a platoon. Being equipped by wireless communication capabilities, these vehicles communicate with one another to maintain their formation as a platoon, hence are cognitive. Autonomous capable vehicles in tightly spaced, computer-controlled platoons will lead to savings in energy due to reduced aerodynamic forces, as well as increased passenger comfort since there will be no sudden accelerations or decelerations. Impacts in the occurrence of collisions, if any, will be very low. The greatest benefit obtained is, however, an increase in highway capacity, along with reduction in traffic congestion, pollution, and energy consumption. Another aspect of this project is the automated electric transportation (AET). This aims at providing energy directly to vehicles from electric highways, thus reducing their energy consumption and CO2 emission. By eliminating the use of overhead wires, infrastructure can be upgraded by electrifying highways and providing energy on demand and in real time to moving vehicles via a wireless energy transfer phenomenon known as wireless inductive coupling. The work done in this research will help to gain an insight into vehicle platooning and the control system related to maintaining the vehicles in this formation

Emerging robot swarm traffic

We discuss traffic patterns generated by swarms of robots while commuting to and from a base station. The overall question is whether to explicitly organise the traffic or whether a certain regularity develops `naturally'. Human driven motorized traffic is rigidly structured in two lanes. However, army ants develop a three-lane pattern in their traffic, while human pedestrians generate a main trail and secondary trials in either direction. Our robot swarm approach is bottom-up: designing individual agents we first investigate the mathematics of cases occurring when applying the artificial potential field method to three 'perfect' robots. We show that traffic lane pattern will not be disturbed by the internal system of forces. Next, we define models of sensor designs to account for the practical fact that robots (and ants) have limited visibility and compare the sensor models in groups of three robots. In the final step we define layouts of a highway: an unbounded open space, a trail with surpassable edges and a hard defined (walled) highway. Having defined the preliminaries we run swarm simulations and look for emerging traffic patterns. Apparently, depending on the initial situation a variety of lane patterns occurs, however, high traffic densities do delay the emergence of traffic lanes considerably. Overall we conclude that regularities do emerge naturally and can be turned into an advantage to obtain efficient robot traffic

Collaborative Verification-Driven Engineering of Hybrid Systems

Hybrid systems with both discrete and continuous dynamics are an important model for real-world cyber-physical systems. The key challenge is to ensure their correct functioning w.r.t. safety requirements. Promising techniques to ensure safety seem to be model-driven engineering to develop hybrid systems in a well-defined and traceable manner, and formal verification to prove their correctness. Their combination forms the vision of verification-driven engineering. Often, hybrid systems are rather complex in that they require expertise from many domains (e.g., robotics, control systems, computer science, software engineering, and mechanical engineering). Moreover, despite the remarkable progress in automating formal verification of hybrid systems, the construction of proofs of complex systems often requires nontrivial human guidance, since hybrid systems verification tools solve undecidable problems. It is, thus, not uncommon for development and verification teams to consist of many players with diverse expertise. This paper introduces a verification-driven engineering toolset that extends our previous work on hybrid and arithmetic verification with tools for (i) graphical (UML) and textual modeling of hybrid systems, (ii) exchanging and comparing models and proofs, and (iii) managing verification tasks. This toolset makes it easier to tackle large-scale verification tasks