Duckietown: An Innovative Way to Teach Autonomy

Teaching robotics is challenging because it is a multidisciplinary, rapidly evolving and experimental discipline that integrates cutting-edge hardware and software. This paper describes the course design and first implementation of Duckietown, a vehicle autonomy class that experiments with teaching innovations in addition to leveraging modern educational theory for improving student learning. We provide a robot to every student, thanks to a minimalist platform design, to maximize active learning; and introduce a role-play aspect to increase team spirit, by modeling the entire class as a fictional start-up (Duckietown Engineering Co.). The course formulation leverages backward design by formalizing intended learning outcomes (ILOs) enabling students to appreciate the challenges of: (a) heterogeneous disciplines converging in the design of a minimal self-driving car, (b) integrating subsystems to create complex system behaviors, and (c) allocating constrained computational resources. Students learn how to assemble, program, test and operate a self-driving car (Duckiebot) in a model urban environment (Duckietown), as well as how to implement and document new features in the system. Traditional course assessment tools are complemented by a full scale demonstration to the general public. The “duckie” theme was chosen to give a gender-neutral, friendly identity to the robots so as to improve student involvement and outreach possibilities. All of the teaching materials and code is released online in the hope that other institutions will adopt the platform and continue to evolve and improve it, so to keep pace with the fast evolution of the field.National Science Foundation (U.S.) (Award IIS #1318392)National Science Foundation (U.S.) (Award #1405259

Safety-Critical Traffic Control by Connected Automated Vehicles

Connected automated vehicles (CAVs) have shown great potential in improving traffic throughput and stability. Although various longitudinal control strategies have been developed for CAVs to achieve string stability in mixed-autonomy traffic systems, the potential impact of these controllers on safety has not yet been fully addressed. This paper proposes safety-critical traffic control (STC) by CAVs -- a strategy that allows a CAV to stabilize the traffic behind it, while maintaining safety relative to both the preceding vehicle and the following connected human-driven vehicles (HDVs). Specifically, we utilize control barrier functions (CBFs) to impart collision-free behavior with formal safety guarantees to the closed-loop system. The safety of both the CAV and HDVs is incorporated into the framework through a quadratic program-based controller, that minimizes deviation from a nominal stabilizing traffic controller subject to CBF-based safety constraints. Considering that some state information of the following HDVs may be unavailable to the CAV, we employ state observer-based CBFs for STC. Finally, we conduct extensive numerical simulations -- that include vehicle trajectories from real data -- to demonstrate the efficacy of the proposed approach in achieving string stable and, at the same time, provably safe traffic

Efficient Algorithms for Collision Avoidance at Intersections

We consider the problem of synthesising the least restrictive controller for collision avoidance of multiple vehicles at an intersection. The largest set of states for which there exists a control that avoids collisions is known as the maximal controlled invariant set. Exploiting results from the scheduling literature we prove that, for a general model of vehicle dynamics at an intersection, the problem of checking membership in the maximal controlled invariant set is NP-hard. We then describe an algorithm that solves this problem approximately and with provable error bounds. The approximate solution is used to design a supervisor for collision avoidance whose complexity scales polynomially with the number of vehicles. The supervisor is based on a hybrid algorithm that employs a dynamic model of the vehicles and periodically solves a scheduling problem

Stochastic hybrid models for predicting the behavior of drivers facing the yellow-light-dilemma

We address the problem of predicting whether a driver facing the yellow-light-dilemma will cross the intersection with the red light. Based on driving simulator data, we propose a stochastic hybrid system model for driver behavior. Using this model combined with Gaussian process estimation and Monte Carlo simulations, we obtain an upper bound for the probability of crossing with the red light. This upper bound has a prescribed confidence level and can be calculated quickly on-line in a recursive fashion as more data become available. Calculating also a lower bound we can show that the upper bound is on average less than 3% higher than the true probability. Moreover, tests on driving simulator data show that 99% of the actual red light violations, are predicted to cross on red with probability greater than 0.95 while less than 5% of the compliant trajectories are predicted to have an equally high probability of crossing. Determining the probability of crossing with the red light will be important for the development of warning systems that prevent red light violations

Design of Driver-Assist Systems Under Probabilistic Safety Specifications Near Stop Signs

In this paper, we consider the problem of designing in-vehicle driver-assist systems that warn or override the driver to prevent collisions with a guaranteed probability. The probabilistic nature of the problem naturally arises from many sources of uncertainty, among which the behavior of the surrounding vehicles and the response of the driver to on-board warnings. We formulate this problem as a control problem for uncertain systems under probabilistic safety specifications and leverage the structure of the application domain to reach computationally efficient implementations. Simulations using a naturalistic data set show that the empirical probability of safety is always within 5% of the theoretical value in the case of direct driver override. In the case of on-board warnings, the empirical value is more conservative due primarily to drivers decelerating more strongly than requested. However, the empirical value is greater than or equal to the theoretical value, demonstrating a clear safety benefit

Safety-critical optimal control in autonomous traffic systems

Traffic congestion is a central problem in transportation systems, especially in urban areas. The rapid development of Connected and Automated Vehicles (CAVs) and new traffic infrastructure technologies provides a promising solution to solve this problem. This work focuses on the safety-critical optimal control of CAVs in autonomous traffic systems. The dissertation starts with the roundabout problem of controlling CAVs travelling through a roundabout so as to jointly minimize their travel time, energy consumption, and centrifugal discomfort while providing speed-dependent safety guarantees. A systematic approach is developed to determine the safety constraints for each CAV dynamically. The joint optimal control and control barrier function (OCBF) controller is applied, where the unconstrained optimal control solution is derived which is subsequently optimally tracked by a real-time controller while guaranteeing the satisfaction of all safety constraints. Secondly, the dissertation deals with the feasibility problem of OCBF. The feasibility problem arises when the control bounds conflict with the Control Barrier Function (CBF) constraints and is solved by adding a single feasibility constraint to the Quadratic Problem (QP) in the OCBF controller to derive the feasibility guaranteed OCBF. The feasibility guaranteed OCBF is applied in the merging control problem which provably guarantees the feasibility of all QPs derived from the OCBF controller. Thirdly, the dissertation deals with the performance loss of OCBF due to the improperly selected reference trajectory which deviates largely from the complete optimal solution especially when the vehicle limitations are tight. A neural network is used to learn the control policy from data retrieved by offline calculation from the complete optimal solutions. Tracking the learnt reference trajectory with CBF outperforms OCBF in simulation experiments. Finally, a hierarchical framework of modular control zones (CZ) is proposed to extend the safety-critical optimal control of CAV from a single CZ to a traffic network. The hierarchical modular CZ framework is developed consisting of a lower-level OCBF controller and a higher-level feedback flow controller to coordinate adjacent CZs which outperforms a direct extension of the OCBF framework to multiple CZs without any flow control in simulation

Motion Planning and Safety for Autonomous Driving

This thesis discusses two different problems in motion planning for autonomous driving. The first is the problem of optimizing a lattice planner control set for any particular autonomous driving task, with the goal of reducing planning time for that task. The driving task is encoded in the form of a dataset of trajectories executed while performing said task. In addition to improving planning time, the optimized control set should capture the driving style of the dataset. In this sense, the control set is learned from the data and is tailored to a particular task. To determine the value of control actions to add to the control set, a modified version of the Fréchet distance is used to score how useful control actions are for generating paths similar to those in the dataset. This method is then compared to the state of the art lattice planner control set optimization technique in terms of planning runtime for the learned task. The second problem is the task of extending the Responsibility-Sensitive Safety (RSS) framework by introducing swerve manoeuvres in addition to the nominal braking manoeu- vres present in the framework. This includes comparing the clearance distances required by a swerve to the braking distances in the original framework. This comparison shows that swerve manoeuvres require less distance gap in order to reach safety from a braking agent in front of the autonomous vehicle at higher speeds. For more realistic swerve manoeuvres, the kinematic bicycle model is used rather than the 2-D double integrator model consid- ered in RSS. An upper bound is then computed on the required clearance distance for a swerve manoeuvre that satisfies bicycle kinematics. A longitudinal safe following distance is then derived that is provably safe, and is shown to be lower than the following distance required by RSS at higher speeds. The use of the kinematic bicycle model is then validated by computing swerve manoeuvres with a dynamic single-track car model and Pacejka tire model, and comparing the single-track swerves to the bicycle swerves

Safety Control of a Class of Stochastic Order Preserving Systems with Application to Collision Avoidance near Stop Signs

Abstract-In this paper, we consider the problem of keeping the state of a system outside of an undesired set of states with probability at least P. We focus on a class of order preserving systems with a constant input disturbance that is extracted from a known probability distribution. Leveraging the structure of the system, we construct an explicit supervisor that guarantees the system state to be kept outside the undesired set with at least probability P. We apply this supervisor to a collision avoidance problem, where a semi-autonomous vehicle is engaged in preventing a rear-end collision with a preceding human-driven vehicle, while stopping at a stop sign. We apply the designed supervisor in simulations in which the preceding vehicle trajectories are taken from a test data set. Using this data, we demonstrate experimentally that the probability of preventing a rear-end collision while stopping at the stop sign is at least P, as expected from theory. The simulation results further show that this probability is very close to P, indicating that the supervisor is not conservative