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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
Multi-Agent Chance-Constrained Stochastic Shortest Path with Application to Risk-Aware Intelligent Intersection
In transportation networks, where traffic lights have traditionally been used
for vehicle coordination, intersections act as natural bottlenecks. A
formidable challenge for existing automated intersections lies in detecting and
reasoning about uncertainty from the operating environment and human-driven
vehicles. In this paper, we propose a risk-aware intelligent intersection
system for autonomous vehicles (AVs) as well as human-driven vehicles (HVs). We
cast the problem as a novel class of Multi-agent Chance-Constrained Stochastic
Shortest Path (MCC-SSP) problems and devise an exact Integer Linear Programming
(ILP) formulation that is scalable in the number of agents' interaction points
(e.g., potential collision points at the intersection). In particular, when the
number of agents within an interaction point is small, which is often the case
in intersections, the ILP has a polynomial number of variables and constraints.
To further improve the running time performance, we show that the collision
risk computation can be performed offline. Additionally, a trajectory
optimization workflow is provided to generate risk-aware trajectories for any
given intersection. The proposed framework is implemented in CARLA simulator
and evaluated under a fully autonomous intersection with AVs only as well as in
a hybrid setup with a signalized intersection for HVs and an intelligent scheme
for AVs. As verified via simulations, the featured approach improves
intersection's efficiency by up to while also conforming to the
specified tunable risk threshold
LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving
Self-driving vehicles need to anticipate a diverse set of future traffic
scenarios in order to safely share the road with other traffic participants
that may exhibit rare but dangerous driving. In this paper, we present LookOut,
an approach to jointly perceive the environment and predict a diverse set of
futures from sensor data, estimate their probability, and optimize a
contingency plan over these diverse future realizations. In particular, we
learn a diverse joint distribution over multi-agent future trajectories in a
traffic scene that allows us to cover a wide range of future modes with high
sample efficiency while leveraging the expressive power of generative models.
Unlike previous work in diverse motion forecasting, our diversity objective
explicitly rewards sampling future scenarios that require distinct reactions
from the self-driving vehicle for improved safety. Our contingency planner then
finds comfortable trajectories that ensure safe reactions to a wide range of
future scenarios. Through extensive evaluations, we show that our model
demonstrates significantly more diverse and sample-efficient motion forecasting
in a large-scale self-driving dataset as well as safer and more comfortable
motion plans in long-term closed-loop simulations than current state-of-the-art
models
Learning-Aware Safety for Interactive Autonomy
One of the outstanding challenges for the widespread deployment of robotic
systems like autonomous vehicles is ensuring safe interaction with humans
without sacrificing efficiency. Existing safety analysis methods often neglect
the robot's ability to learn and adapt at runtime, leading to overly
conservative behavior. This paper proposes a new closed-loop paradigm for
synthesizing safe control policies that explicitly account for the system's
evolving uncertainty under possible future scenarios. The formulation reasons
jointly about the physical dynamics and the robot's learning algorithm, which
updates its internal belief over time. We leverage adversarial deep
reinforcement learning (RL) for scaling to high dimensions, enabling tractable
safety analysis even for implicit learning dynamics induced by state-of-the-art
prediction models. We demonstrate our framework's ability to work with both
Bayesian belief propagation and the implicit learning induced by a large
pre-trained neural trajectory predictor.Comment: Conference on Robot Learning 202
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