Safe Navigation of Autonomous Vehicles in Structured Mixed-Traffic Environments
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The primary driving force behind autonomous vehicle (AV) research is the prospect of enhancing road safety by preventing accidents caused by human errors. To that end, it seems rather improbable that AVs will replace all human-driven vehicles in the near future. The more plausible scenario is that AVs will gradually be introduced on public roads and highways in the presence of human-driven vehicles, leading to mixed-traffic scenarios. In addition to the existing challenges associated with autonomous driving stemming from various uncertainty factors associated with sensing, prediction, control, and computation, these situations pose further difficulties pertaining to the variability in human driving patterns. Therefore, to ensure widespread public acceptance of AVs, it is crucial to develop planning and decision-making algorithms, while benefiting from modern sensing, computation, and control methods, that can deliver safe, efficient, and reliable performance in mixed-traffic situations.
Considering the need to cater to the behavior variability of human drivers, we address the joint decision-making and motion planning problem in structured environments with a multi-timescale navigation architecture. Specifically, we design algorithms for commonly encountered highway driving scenarios that require effective real-time decision-making, reliable motion prediction of on-road entities, behavior consideration of on-road agents, and attention to safety as well as passenger comfort. The specific problems addressed in this dissertation include bidirectional highway overtaking, highway maneuvering in traffic, and crash mitigation on highways.
In the proposed framework, we first identify and exploit the different timescales involved in the navigation architecture. Then, we propose algorithmic modules while pursuing systematic complexity (data and computation) reduction at different timescales to gain immediate performance improvements in inference and action/response delay minimization. This leads to real-time situation assessment, computation, and action/control, allowing us to satisfy some of the key requirements for autonomous driving algorithms. Notably, the algorithms proposed in this dissertation ensure that the safety of the overall system is a fundamental constraint built into the system. Distinctive features of the proposed approaches include real-time operation capability, consideration for behavior variability of on-road agents, modularity in design, and optimality with respect to various metrics.
The algorithms developed and implemented as part of this dissertation fundamentally rely upon the application of optimization techniques in a receding horizon fashion which allows for optimality in performance while explicitly accounting for actuation limits, vehicle dynamics, and safety. Even though the modularity of the proposed navigation framework allows for the incorporation of modern prediction and control methods, we develop various prediction modules for the trajectory prediction of on-road agents. We further benefit from risk evaluation methodologies to ensure robustness to behavior variability of human drivers on the road and handle collision-prone situations.
In the spirit of emulating real-world situations, we place special emphasis on utilizing realistic driving simulations that capture the effects of communication delays between different modules, limitations in computation resources, and randomization of scenarios. For the developed algorithms, we evaluate the performance in comparative singular case studies as well as randomized Monte Carlo simulations with respect to several metrics to assess the efficacy of the developed methods.