157 research outputs found
Simulation and Learning for Urban Mobility: City-scale Traffic Reconstruction and Autonomous Driving
Traffic congestion has become one of the most critical issues worldwide. The costs due to traffic gridlock and jams are approximately $160 billion in the United States, more than £13 billion in the United Kingdom, and over one trillion dollars across the globe annually. As more metropolitan areas will experience increasingly severe traffic conditions, the ability to analyze, understand, and improve traffic dynamics becomes critical. This dissertation is an effort towards achieving such an ability. I propose various techniques combining simulation and machine learning to tackle the problem of traffic from two perspectives: city-scale traffic reconstruction and autonomous driving. Traffic, by its definition, appears in an aggregate form. In order to study it, we have to take a holistic approach. I address the problem of efficient and accurate estimation and reconstruction of city-scale traffic. The reconstructed traffic can be used to analyze congestion causes, identify network bottlenecks, and experiment with novel transport policies. City-scale traffic estimation and reconstruction have proven to be challenging for two particular reasons: first, traffic conditions that depend on individual drivers are intrinsically stochastic; second, the availability and quality of traffic data are limited. Traditional traffic monitoring systems that exist on highways and major roads can not produce sufficient data to recover traffic at scale. GPS data, in contrast, provide much broader coverage of a city thus are more promising sources for traffic estimation and reconstruction. However, GPS data are limited by their spatial-temporal sparsity in practice. I develop a framework to statically estimate and dynamically reconstruct traffic over a city-scale road network by addressing the limitations of GPS data. Traffic is also formed of individual vehicles propagating through space and time. If we can improve the efficiency of them, collectively, we can improve traffic dynamics as a whole. Recent advancements in automation and its implication for improving the safety and efficiency of the traffic system have prompted widespread research of autonomous driving. While exciting, autonomous driving is a complex task, consider the dynamics of an environment and the lack of accurate descriptions of a desired driving behavior. Learning a robust control policy for driving remains challenging as it requires an effective policy architecture, an efficient learning mechanism, and substantial training data covering a variety of scenarios, including rare cases such as accidents. I develop a framework, named ADAPS (Autonomous Driving via Principled Simulations), for producing robust control policies for autonomous driving. ADAPS consists of two simulation platforms which are used to generate and analyze simulated accidents while automatically generating labeled training data, and a hierarchical control policy which takes into account the features of driving behaviors and road conditions. ADAPS also represents a more efficient online learning mechanism compared to previous techniques, in which the number of iterations required to learn a robust control policy is reduced.Doctor of Philosoph
Understanding and Developing Equitable and Fair Transportation Systems
The transportation system is an interplay between infrastructure, vehicles,
and policy. During the past century, the rapid expansion of the road network,
blended with increasing vehicle production and mobility demands, has been
stressing the system's capacity and resulting in a shocking amount of annual
costs. To alleviate these costs while providing passengers with safe and
efficient travel experiences, we need to better design and plan our
transportation system. To start with, not only the design of our road network
is topologically flawed but also our infrastructure likely facilitates
inequality: roads and bridges are found to better connect affluent sectors
while excluding the poor. While technological advancements such as connected
and autonomous vehicles (CAVs) and novel operation modes such as shared economy
have offered new opportunities, questions remain. First, what is the
relationship between the road network, community development, demographics, and
mobility behaviors? Second, by leveraging the insights from studying the first
question, can we better plan, coordinate, and optimize vehicles in different
modalities such as human-driven and autonomous to construct safe, efficient,
and resilient traffic flows? Third, how can we build an intelligent
transportation system to promote equity and fairness in our community
development? This proposal is the first step toward answering these questions
Spatio-temporal Keyframe Control of Traffic Simulation using Coarse-to-Fine Optimization
We present a novel traffic trajectory editing method which uses
spatio-temporal keyframes to control vehicles during the simulation to generate
desired traffic trajectories. By taking self-motivation, path following and
collision avoidance into account, the proposed force-based traffic simulation
framework updates vehicle's motions in both the Frenet coordinates and the
Cartesian coordinates. With the way-points from users, lane-level navigation
can be generated by reference path planning. With a given keyframe, the
coarse-to-fine optimization is proposed to efficiently generate the plausible
trajectory which can satisfy the spatio-temporal constraints. At first, a
directed state-time graph constructed along the reference path is used to
search for a coarse-grained trajectory by mapping the keyframe as the goal.
Then, using the information extracted from the coarse trajectory as
initialization, adjoint-based optimization is applied to generate a finer
trajectory with smooth motions based on our force-based simulation. We validate
our method with extensive experiments
TraInterSim: Adaptive and Planning-Aware Hybrid-Driven Traffic Intersection Simulation
Traffic intersections are important scenes that can be seen almost everywhere
in the traffic system. Currently, most simulation methods perform well at
highways and urban traffic networks. In intersection scenarios, the challenge
lies in the lack of clearly defined lanes, where agents with various motion
plannings converge in the central area from different directions. Traditional
model-based methods are difficult to drive agents to move realistically at
intersections without enough predefined lanes, while data-driven methods often
require a large amount of high-quality input data. Simultaneously, tedious
parameter tuning is inevitable involved to obtain the desired simulation
results. In this paper, we present a novel adaptive and planning-aware
hybrid-driven method (TraInterSim) to simulate traffic intersection scenarios.
Our hybrid-driven method combines an optimization-based data-driven scheme with
a velocity continuity model. It guides the agent's movements using real-world
data and can generate those behaviors not present in the input data. Our
optimization method fully considers velocity continuity, desired speed,
direction guidance, and planning-aware collision avoidance. Agents can perceive
others' motion planning and relative distance to avoid possible collisions. To
preserve the individual flexibility of different agents, the parameters in our
method are automatically adjusted during the simulation. TraInterSim can
generate realistic behaviors of heterogeneous agents in different traffic
intersection scenarios in interactive rates. Through extensive experiments as
well as user studies, we validate the effectiveness and rationality of the
proposed simulation method.Comment: 13 pages, 12 figure
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