Interactive Prediction and Planning for Autonomous Driving: from Algorithms to Fundamental Aspects

Inevitably, autonomous vehicles need to interact with other road participants in a variety of highly complex or critical driving scenarios. It is still an extremely challenging task even for the forefront companies or institutes to enable autonomous vehicles to interactively predict the behavior of others, and plan safe and high-quality motions accordingly. The major obstacles are not just originated from prediction and planning algorithms with insufficient performances. Several fundamental problems in the fields of interactive prediction and planning still remain open, such as formulation, representation and evaluation of interactive prediction methods, motion dataset with densely interactive driving behavior, as well as interface of interactive prediction and planning algorithms. The aforementioned fundamental aspects of interactive prediction and planning are addressed in this dissertation along with various kinds of algorithms. First, generic environmental representation for various scenarios with topological decomposition is constructed, and a corresponding planning algorithm is designed by combining graph search and optimization. Hard constraints in optimization-based planners are also incorporated into the training loss of imitation learning so that the policy net can generate safe and feasible motions in highly constrained scenarios. Unified problem formulation and motion representation are designed for different paradigms of interactive predictors such as planning-based prediction (inverse reinforcement learning), as well as probabilistic graphical models (hidden Markov model) and deep neural networks (mixture density network), which are utilized for the prediction/planning interface design and prediction benchmark. A framework combing decision network and graph-search/optimization/sample-based planner is proposed to achieve a driving strategy which is defensive to potential violations of others, but not overly conservatively to threats of low probabilities. Such driving strategy is achieved via experiments based on the aforementioned interactive prediction and planning algorithms with proper interface designed. These predictors are also evaluated from closed loop perspective considering planning fatality when using the prediction results instead of pure data approximation metrics. Finally, INTERACTION (INTERnational, Adversarial and Cooperative moTION) dataset with highly interactive driving scenarios and behavior from international locations is constructed with interaction density metric defined to compare different datasets. The dataset has been utilized for various behavior-related research areas such as prediction, planning, imitation learning and behavior modeling, and is inspiring new research fields such as representation learning, interaction extraction and scenario generation

Naturalistic Driver Intention and Path Prediction using Machine Learning

Autonomous vehicles are still yet to be available to the public. This is because there are a number of challenges that have not been overcome to ensure that autonomous vehicles can safely and efficiently drive on public roads. Accurate prediction of other vehicles is vital for safe driving, as interacting with other vehicles is unavoidable on public streets. This thesis explores reasons why this problem of scene understanding is still unsolved, and presents methods for driver intention and path prediction. The thesis focuses on intersections, as this is a very complex scenario in which to predict the actions of human drivers. There is very limited data available for intersection studies from the perspective of an autonomous vehicle. This thesis presents a very large dataset of over 23,000 vehicle trajectories, used to validate the algorithms presented in this thesis. This dataset was collected using a lidar based vehicle detection and tracking system onboard a vehicle. Analytics of this data is presented. To determine the intent of vehicle at an intersection, a method for manoeuvre classification through the use of recurrent neural networks is presented. This allows accurate predictions of which destination a vehicle will take at an unsignalised intersection, based on that vehicle's approach. The final contribution of this thesis presents a method for driver path prediction, based on recurrent neural networks. It produces a multi-modal prediction for the vehicle’s path with uncertainty assigned to each mode. The output modes are not hand labelled, but instead learned from the data. This results in there not being a fixed number of output modes. Whilst the application of this method is vehicle prediction, this method shows significant promise to be used in other areas of robotics

A Study of Readiness for Transportation Electrification and Automation Focusing on Safety and Future Adoption

Transportation electrification and automation are growing societal trends and considered promising pathways to enhance the safety, mobility, efficiency, and sustainability of the surface transportation system. At this early stage of transportation electrification and automation, one of the most critical issues is whether and to what extent people are willing to adopt electric vehicle (EV) and automated vehicle (AV) technologies in the future. Another critical issue, especially concerning transportation automation, is how to thoroughly ensure the safety of automated driving performance to resolve safety concerns about AVs, which is one of the key challenges to AV adoption. In this regard, the dissertation aims to provide new knowledge and deep insights regarding the readiness for transportation electrification and automation in terms of safety and future adoption by investigating how different types of travelers are willing to embrace EV and AV technologies and what safety-related challenges the automated driving systems are facing. First, the dissertation systematically analyzes how individuals become inclined to use AV-based travel options and adopt alternative fuel vehicles (AFVs). For this, an “AV inclination index” is developed to quantify individual travelers’ inclination toward AV-based travel options encompassing owning an AV, using AV ride-hailing services, and using Shared AV (SAV) ride-hailing services. Importantly, the dissertation reveals a meaningful relationship between the “AV inclination index” and AFV adoption. Considering that the commercial sector has the potential to adopt a considerable amount of EVs in the future, the dissertation explores commercial light-duty fleet owners’ intention to adopt different types of EVs. Paying attention to early adopters’ experiences and perspectives, the dissertation investigates BEV owners’ satisfaction and willingness to repurchase a BEV in the future. Given that the safety of AVs is one of the critical factors associated with individual travelers’ willingness to use AVs in the future, the dissertation performs an exhaustive analysis of crashes involving AVs tested on public roads to provide a better understanding of AV safety performance. Based on the findings from each chapter, the dissertation provides the vehicle and transportation industries, engineers, planners, and policymakers with practical implications for a smooth transition to transportation electrification and automation