Limitations and Improvements of the Intelligent Driver Model (IDM)

This contribution analyzes the widely used and well-known "intelligent driver model" (briefly IDM), which is a second order car-following model governed by a system of ordinary differential equations. Although this model was intensively studied in recent years for properly capturing traffic phenomena and driver braking behavior, a rigorous study of the well-posedness of solutions has, to our knowledge, never been performed. First it is shown that, for a specific class of initial data, the vehicles' velocities become negative or even diverge to $-\infty$ in finite time, both undesirable properties for a car-following model. Various modifications of the IDM are then proposed in order to avoid such ill-posedness. The theoretical remediation of the model, rather than post facto by ad-hoc modification of code implementations, allows a more sound numerical implementation and preservation of the model features. Indeed, to avoid inconsistencies and ensure dynamics close to the one of the original model, one may need to inspect and clean large input data, which may result practically impossible for large-scale simulations. Although well-posedness issues occur only for specific initial data, this may happen frequently when different traffic scenarios are analyzed, and especially in presence of lane-changing, on ramps and other network components as it is the case for most commonly used micro-simulators. On the other side, it is shown that well-posedness can be guaranteed by straight-forward improvements, such as those obtained by slightly changing the acceleration to prevent the velocity from becoming negative.Comment: 29 pages, 23 Figure

Automation of vehicular systems using deep reinforcement learning and mean-field models: Application to heavy duty trucks

The transportation sector consumes about a third of all energy consumed in the world, about a third of which is consumed by trucks. Future transportation systems must address this energy challenge, in addition to the other inefficiencies related to time, money, and lives lost while the system is operating. Vehicle automation is one of the promising opportunities underway. For instance, cooperative adaptive cruise control, an extension of the more popular cruise control and adaptive cruise control systems, promises to reduce fuel consumption by up to 15% for participating trucks, reduce emissions, increase road capacity at high technology penetration rates, and contribute to road safety. Heavy duty trucks are complex vehicles that are designed and built for specific mission requirements. Any of these trucks could be equipped from a wide selection of vehicle components with a significantly wide spectrum of operating dynamics and performances. Driving a heavy duty truck is an equally complex task. Human drivers must be well educated and trained about the specific truck they are about to drive and operate. They must optimize in real-time for factors such as truck dynamics and driving performance; road, truck, and payload safety; truck operation economics; truck driving law constraints; mission constraints; in addition to background traffic on the road. Automation of heavy duty truck operation tasks require equally advanced engineering tools. For instance, high precision modeling and control have historically required a detailed study of each subject truck. This thesis presents a process using deep learning and deep reinforcement learning for microscopic longitudinal modeling and control of such trucks that is agnostic to their internal mechanics. The process is demonstrated and evaluated for several truck mechanical configurations using high fidelity simulation and in the field. Cruise control of single truck operations has been considered, in addition to cooperative adaptive cruise control for multi-truck coordination. Long haul heavy duty trucks often drive within shared road infrastructure with background traffic. To account for this traffic on the road, we consider multi-scale partial differential equation mean-field models. With this approach, each truck is modeled microscopically while background traffic is modeled mesoscopically. A nondissipative numerical solver is developed and evaluated for computational study of these models. The solver maintains structure and resolution at a wide range of discretization resolutions suitable for development of optimal control laws. This thesis investigates computational methods for the automation of heavy duty trucks. While vehicle driving automation is already underway, more investigation is still required to bring about full autonomy. The future of the transportation system and trucking could benefit from further study and development of the sciences and engineering of autonomy with consideration to the complex interplay between the vehicle as an agent, the transportation system as an operations context, the logistics system as a mission context, and the human beneficiary

A holistic approach to the energy-efficient smoothing of traffic via autonomous vehicles

International audienceThe technological advancement in terms of vehicle on-board sensors and actuators, as well as for infrastructures, open an unprecedented scenario for the management of vehicular traffic. We focus on the problem of smoothing traffic by controlling a small number of autonomous vehicles immersed in the bulk traffic stream. Specifically, we aim at dissipating stop-and-go waves, which are ubiquitous and proven to increase fuel consumption tremendously and reduce. Our approach is holistic, as it is based on a large collaborative effort, which ranges from mathematical models for traffic and control all the way to building infrastructures capable of measuring energy efficiency and providing real-time data. Such an approach allows to clearly set and measure a metric for success in the form of a reduction of at least 10% of fuel consumption using 5% of autonomous vehicles immersed in bulk traffic. The chapter illustrates the overall approach and provides simulation results on a tuned microsimulator for the California I-210