Motion planning for autonomous robots and vehicles in presence of uncontrolled agents remains a challenging problem as the reactive behaviors of the uncontrolled agents must be considered. Since the uncontrolled agents usually demonstrate multimodal reactive behavior, the motion planner needs to solve a continuous motion planning problem under these behaviors, which contains a discrete element. We propose a branch Model Predictive Control (MPC) framework that plans over feedback policies to leverage the reactive behavior of the uncontrolled agent. In particular, a scenario tree is constructed from a finite set of policies of the uncontrolled agent, and the branch MPC solves for a feedback policy in the form of a trajectory tree, which shares the same topology as the scenario tree. Moreover, coherent risk measures such as the Conditional Value at Risk (CVaR) are used as a tuning knob to adjust the tradeoff between performance and robustness. The proposed branch MPC framework is tested on an overtake and lane change task and a merging task for autonomous vehicles in simulation, and on the motion planning of an autonomous quadruped robot alongside an uncontrolled quadruped in experiments. The result demonstrates interesting human-like behaviors, achieving a balance between safety and performance.

Interactive multi-modal motion planning with Branch Model Predictive Control.

Motion planning in environments with multiple agents is critical to many important autonomous applications such as autonomous vehicles and assistive robots. This paper considers the problem of motion planning, where the controlled agent shares the environment with multiple uncontrolled agents. First, a predictive model of the uncontrolled agents is trained to predict all possible trajectories within a short horizon based on the scenario. The prediction is then fed to a motion planning module based on model predictive control. We proved generalization bound for the predictive model using three different methods, post-bloating, support vector machine (SVM), and conformal analysis, all capable of generating stochastic guarantees of the correctness of the predictor. The proposed approach is demonstrated in simulation in a scenario emulating autonomous highway driving.

Reactive motion planning with probabilistic safety guarantees

In public vehicles, one of the major concerns is driver's level of expertise for its direct proportionality to safety of passengers. Hence, before a driver is subjected to certain type of vehicle, he should be thoroughly evaluated and categorized with respect to certain parameters instead of only one‐time metric of having driving license. These aspects may be driver's expertise, vigilance, aptitude, experience years, cognition, driving style, formal education, terrain, region, minor violations, major accidents, and age group. The purpose of this categorization is to ascertain suitability of a driver for certain vehicle type(s) to ensure passengers' safety. Currently, no driver categorization technique fully comprehends the implicit as well as explicit characteristics of drivers dynamically. In this paper, machine learning–based dynamic and adaptive technique named D‐CHAITs (driver categorization through hybrid of artificial intelligence techniques) is proposed for driver categorization with an objective focus on driver's attributes modeled in DriverOntology. A supervised mode of learning has been employed on a labeled dataset, having diverse profiles of drivers with attributes pertinent to drivers' perspectives of demographics, behaviors, expertise, and inclinations. A comparative analysis of D‐CHAIT with three other machine learning techniques (fuzzy logic, case‐based reasoning, and artificial neural networks) is also presented. The efficacy of all techniques was empirically measured while categorizing the drivers based on their profiles through metrics of accuracy, precision, recall, F‐measure performance, and associated costs. These empirical quantifications assert D‐CHAIT as a better technique than contemporary ones. The novelty of proposed technique is signified through preprocessing of feature attributes, quality of data, training of machine learning model on more relevant data, and adaptivity

This is the peer reviewed version of the following article: Context aware ontology‐based hybrid intelligent framework for vehicle driver categorization, which has been published in final form at https://doi.org/10.1002/ett.3729. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.

/pdf/context-aware-ontology-based-hybrid-intelligent-framework-3ov8v1xf9f.pdf

Context aware ontology‐based hybrid intelligent framework for vehicle driver categorization

There is a growing interest in building autonomous systems that interact with complex environments. The difficulty associated with obtaining an accurate model for such environments poses a challenge to the task of assessing and guaranteeing the system's performance. We present a data-driven solution that allows for a system to be evaluated for specification conformance without an accurate model of the environment. Our approach involves learning a conservative reactive bound of the environment's behavior using data and specification of the system's desired behavior. First, the approach begins by learning a conservative reactive bound on the environment's actions that captures its possible behaviors with high probability. This bound is then used to assist verification, and if the verification fails under this bound, the algorithm returns counter-examples to show how failure occurs and then uses these to refine the bound. We demonstrate the applicability of the approach through two case-studies: i) verifying controllers for a toy multi-robot system, and ii) verifying an instance of human-robot interaction during a lane-change maneuver given real-world human driving data.

Counter-example Guided Learning of Bounds on Environment Behavior

This paper proposes a guardian architecture, consisting of an estimation and a supervisor module providing a set of inputs that guarantees safety, in driving scenarios. The main idea is to offline compute a library of robust controlled invariant sets (RCIS), for each possible driver intention model of the other vehicles, together with an intention-agnostic albeit conservative RCIS. At runtime, when the intention estimation module determines which driver model the other vehicles are following, the appropriate RCIS is chosen to provide the safe and less conservative input set for supervision. We show that the composition of the intention estimation module with the proposed intention-aware supervisor module is safe. Moreover, we show how to compute intention-agnostic and intention-specific RCIS by growing an analytically found simple invariant safe set. The results are demonstrated on a case study on how to safely interact with a human-driven car on a highway scenario, using data collected from a driving simulator.

Intention-Aware Supervisory Control with Driving Safety Applications

This paper discusses a novel approach to model human driver behavior. A classification-based method is proposed to construct a reactive bound on possible human driving actions given the scenario description (such as the vehicle states and the behavior of surrounding vehicles). This approach captures the reactiveness and uncertainty of human drivers. Real human driving data is used as the positive training data, while dangerous actions sampled via a Hamilton Jacobi reachability computation constitute the negative training data. A classifier that separates the two groups is then trained via a customized $\mathcal{L}_{1}$ . Support Vector Machine (SVM), and an analytical bound function is derived from the classifier which maps the state and surrounding vehicles' actions to the bound on possible actions of the human driver. The credibility of the proposed approach is analyzed under the random convex optimization framework. Potential applications of this work include the computation of safe sets, synthesis of safety guaranteed controllers for systems interacting with humans such as autonomous vehicles, and evaluation of such systems.

Modelling of uncertain reactive human driving behavior: a classification approach

/pdf/control-theoretic-evaluation-of-policy-in-sequential-qmks1st3mp.pdf

Control-theoretic Evaluation of Policy in Sequential Decision Making via Data-driven Differential Game

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Modelling of uncertain reactive human driving behavior: a classification approach

Control-theoretic Evaluation of Policy in Sequential Decision Making via Data-driven Differential Game