664 research outputs found
Stochastic Model Predictive Control for Autonomous Mobility on Demand
This paper presents a stochastic, model predictive control (MPC) algorithm
that leverages short-term probabilistic forecasts for dispatching and
rebalancing Autonomous Mobility-on-Demand systems (AMoD, i.e. fleets of
self-driving vehicles). We first present the core stochastic optimization
problem in terms of a time-expanded network flow model. Then, to ameliorate its
tractability, we present two key relaxations. First, we replace the original
stochastic problem with a Sample Average Approximation (SAA), and characterize
the performance guarantees. Second, we separate the controller into two
separate parts to address the task of assigning vehicles to the outstanding
customers separate from that of rebalancing. This enables the problem to be
solved as two totally unimodular linear programs, and thus easily scalable to
large problem sizes. Finally, we test the proposed algorithm in two scenarios
based on real data and show that it outperforms prior state-of-the-art
algorithms. In particular, in a simulation using customer data from DiDi
Chuxing, the algorithm presented here exhibits a 62.3 percent reduction in
customer waiting time compared to state of the art non-stochastic algorithms.Comment: Submitting to the IEEE International Conference on Intelligent
Transportation Systems 201
Formal Synthesis of Control Strategies for Positive Monotone Systems
We design controllers from formal specifications for positive discrete-time
monotone systems that are subject to bounded disturbances. Such systems are
widely used to model the dynamics of transportation and biological networks.
The specifications are described using signal temporal logic (STL), which can
express a broad range of temporal properties. We formulate the problem as a
mixed-integer linear program (MILP) and show that under the assumptions made in
this paper, which are not restrictive for traffic applications, the existence
of open-loop control policies is sufficient and almost necessary to ensure the
satisfaction of STL formulas. We establish a relation between satisfaction of
STL formulas in infinite time and set-invariance theories and provide an
efficient method to compute robust control invariant sets in high dimensions.
We also develop a robust model predictive framework to plan controls optimally
while ensuring the satisfaction of the specification. Illustrative examples and
a traffic management case study are included.Comment: To appear in IEEE Transactions on Automatic Control (TAC) (2018), 16
pages, double colum
A Two-Stage Optimization-based Motion Planner for Safe Urban Driving
Recent road trials have shown that guaranteeing the safety of driving
decisions is essential for the wider adoption of autonomous vehicle technology.
One promising direction is to pose safety requirements as planning constraints
in nonlinear, non-convex optimization problems of motion synthesis. However,
many implementations of this approach are limited by uncertain convergence and
local optimality of the solutions achieved, affecting overall robustness. To
improve upon these issues, we propose a novel two-stage optimization framework:
in the first stage, we find a solution to a Mixed-Integer Linear Programming
(MILP) formulation of the motion synthesis problem, the output of which
initializes a second Nonlinear Programming (NLP) stage. The MILP stage enforces
hard constraints of safety and road rule compliance generating a solution in
the right subspace, while the NLP stage refines the solution within the safety
bounds for feasibility and smoothness. We demonstrate the effectiveness of our
framework via simulated experiments of complex urban driving scenarios,
outperforming a state-of-the-art baseline in metrics of convergence, comfort
and progress.Comment: IEEE Transactions on Robotics (T-RO), 202
PILOT: Efficient Planning by Imitation Learning and Optimisation for Safe Autonomous Driving
Achieving the right balance between planning quality, safety and efficiency
is a major challenge for autonomous driving. Optimisation-based motion planners
are capable of producing safe, smooth and comfortable plans, but often at the
cost of runtime efficiency. On the other hand, naively deploying trajectories
produced by efficient-to-run deep imitation learning approaches might risk
compromising safety. In this paper, we present PILOT -- a planning framework
that comprises an imitation neural network followed by an efficient optimiser
that actively rectifies the network's plan, guaranteeing fulfilment of safety
and comfort requirements. The objective of the efficient optimiser is the same
as the objective of an expensive-to-run optimisation-based planning system that
the neural network is trained offline to imitate. This efficient optimiser
provides a key layer of online protection from learning failures or deficiency
on out-of-distribution situations that might compromise safety or comfort.
Using a state-of-the-art, runtime-intensive optimisation-based method as the
expert, we demonstrate in simulated autonomous driving experiments in CARLA
that PILOT achieves a significant reduction in runtime when compared to the
expert it imitates without sacrificing planning quality.Comment: 8 pages, 7 figure
A comprehensive survey on cooperative intersection management for heterogeneous connected vehicles
Nowadays, with the advancement of technology, world is trending toward high mobility and dynamics. In this context, intersection management (IM) as one of the most crucial elements of the transportation sector demands high attention. Today, road entities including infrastructures, vulnerable road users (VRUs) such as motorcycles, moped, scooters, pedestrians, bicycles, and other types of vehicles such as trucks, buses, cars, emergency vehicles, and railway vehicles like trains or trams are able to communicate cooperatively using vehicle-to-everything (V2X) communications and provide traffic safety, efficiency, infotainment and ecological improvements. In this paper, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid) and autonomous intersections and conduct a comprehensive survey on various intersection management methods for heterogeneous connected vehicles (CVs). We consider heterogeneous classes of vehicles such as road and rail vehicles as well as VRUs including bicycles, scooters and motorcycles. All kinds of intersection goals, modeling, coordination architectures, scheduling policies are thoroughly discussed. Signalized and semi-autonomous intersections are assessed with respect to these parameters. We especially focus on autonomous intersection management (AIM) and categorize this section based on four major goals involving safety, efficiency, infotainment and environment. Each intersection goal provides an in-depth investigation on the corresponding literature from the aforementioned perspectives. Moreover, robustness and resiliency of IM are explored from diverse points of view encompassing sensors, information management and sharing, planning universal scheme, heterogeneous collaboration, vehicle classification, quality measurement, external factors, intersection types, localization faults, communication anomalies and channel optimization, synchronization, vehicle dynamics and model mismatch, model uncertainties, recovery, security and privacy
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