Towards Robust Car Following Dynamics Modeling via Blackbox Models: Methodology, Analysis, and Recommendations

The selection of the target variable is important while learning parameters of the classical car following models like GIPPS, IDM, etc. There is a vast body of literature on which target variable is optimal for classical car following models, but there is no study that empirically evaluates the selection of optimal target variables for black-box models, such as LSTM, etc. The black-box models, like LSTM and Gaussian Process (GP) are increasingly being used to model car following behavior without wise selection of target variables. The current work tests different target variables, like acceleration, velocity, and headway, for three black-box models, i.e., GP, LSTM, and Kernel Ridge Regression. These models have different objective functions and work in different vector spaces, e.g., GP works in function space, and LSTM works in parameter space. The experiments show that the optimal target variable recommendations for black-box models differ from classical car following models depending on the objective function and the vector space. It is worth mentioning that models and datasets used during evaluation are diverse in nature: the datasets contained both automated and human-driven vehicle trajectories; the black-box models belong to both parametric and non-parametric classes of models. This diversity is important during the analysis of variance, wherein we try to find the interaction between datasets, models, and target variables. It is shown that the models and target variables interact and recommended target variables don't depend on the dataset under consideration.

Reframing Offline Reinforcement Learning as a Regression Problem

The study proposes the reformulation of offline reinforcement learning as a regression problem that can be solved with decision trees. Aiming to predict actions based on input states, return-to-go (RTG), and timestep information, we observe that with gradient-boosted trees, the agent training and inference are very fast, the former taking less than a minute. Despite the simplification inherent in this reformulated problem, our agent demonstrates performance that is at least on par with established methods. This assertion is validated by testing it across standard datasets associated with D4RL Gym-MuJoCo tasks. We further discuss the agent's ability to generalize by testing it on two extreme cases, how it learns to model the return distributions effectively even with highly skewed expert datasets, and how it exhibits robust performance in scenarios with sparse/delayed rewards.

SCAN: A Spatial Context Attentive Network for Joint Multi-Agent Intent Prediction

Safe navigation of autonomous agents in human centric environments requires the ability to understand and predict motion of neighboring pedestrians. However, predicting pedestrian intent is a complex problem. Pedestrian motion is governed by complex social navigation norms, is dependent on neighbors' trajectories, and is multimodal in nature. In this work, we propose \textbf{SCAN}, a \textbf{S}patial \textbf{C}ontext \textbf{A}ttentive \textbf{N}etwork that can jointly predict socially-acceptable multiple future trajectories for all pedestrians in a scene. SCAN encodes the influence of spatially close neighbors using a novel spatial attention mechanism in a manner that relies on fewer assumptions, is parameter efficient, and is more interpretable compared to state-of-the-art spatial attention approaches. Through experiments on several datasets we demonstrate that our approach can also quantitatively outperform state of the art trajectory prediction methods in terms of accuracy of predicted intent.

ShieldNN: A Provably Safe NN Filter for Unsafe NN Controllers

In this paper, we consider the problem of creating a safe-by-design Rectified Linear Unit (ReLU) Neural Network (NN), which, when composed with an arbitrary control NN, makes the composition provably safe. In particular, we propose an algorithm to synthesize such NN filters that safely correct control inputs generated for the continuous-time Kinematic Bicycle Model (KBM). ShieldNN contains two main novel contributions: first, it is based on a novel Barrier Function (BF) for the KBM model; and second, it is itself a provably sound algorithm that leverages this BF to a design a safety filter NN with safety guarantees. Moreover, since the KBM is known to well approximate the dynamics of four-wheeled vehicles, we show the efficacy of ShieldNN filters in CARLA simulations of four-wheeled vehicles. In particular, we examined the effect of ShieldNN filters on Deep Reinforcement Learning trained controllers in the presence of individual pedestrian obstacles. The safety properties of ShieldNN were borne out in our experiments: the ShieldNN filter reduced the number of obstacle collisions by 99.4%-100%. Furthermore, we also studied the effect of incorporating ShieldNN during training: for a constant number of episodes, 28% less reward was observed when ShieldNN wasn't used during training. This suggests that ShieldNN has the further property of improving sample efficiency during RL training.

Towards Improved Testing For Deep Learning

The growing use of deep neural networks in safety-critical applications makes it necessary to carry out adequate testing to detect and correct any incorrect behavior for corner case inputs before they can be actually used. Deep neural networks lack an explicit control-flow structure, making it impossible to apply to them traditional software testing criteria such as code coverage. In this paper, we examine existing testing methods for deep neural networks, the opportunities for improvement and the need for a fast, scalable, generalizable end-to-end testing method. We also propose a coverage criterion for deep neural networks that tries to capture all possible parts of the deep neural network's logic.