A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning

By Liting Sun, C-T.John Peng, Wei Zhan, Masayoshi Tomizuka. 2017

90 citations recorded by the source corpus.

Safety and efficiency are two key elements for planning and control in autonomous driving. Theoretically, model-based optimization methods, such as Model Predictive Control (MPC), can provide such optimal driving policies. Their computational complexity, however, grows exponentially with horizon length and number of surrounding vehicles. This makes them impractical for real-time implementation, particularly when nonlinear models are considered. To enable a fast and approximately optimal driving policy, we propose a safe imitation framework, which contains two hierarchical layers. The first layer, defined as the policy layer, is represented by a neural network that imitates a long-term expert driving policy via imitation learning. The second layer, called the execution layer, is a short-term model-based optimal controller that tracks and further fine-tunes the reference trajectories proposed by the policy layer with guaranteed short-term collision avoidance. Moreover, to reduce the distribution mismatch between the training set and the real world, Dataset Aggregation is utilized so that the performance of the policy layer can be improved from iteration to iteration. Several highway driving scenarios are demonstrated in simulations, and the results show that the proposed framework can achieve similar performance as sophisticated long-term optimization approaches but with significantly improved computational efficiency.

Subfields: Reinforcement Learning

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Citations in the corpus

Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems (2020, background): “Theoretical analysis of model-based policy learning can provide bounds on the error incurred from the distributional shift due to the divergence between the learned policy π(a|s) and the behavior policy πβ(a|s) (Sun et al., 2018b; Luo et al., 2018; Janner et al., 2019).”
Deep Imitative Models for Flexible Inference, Planning, and Control (2018, background): “While our target setting is ofﬂine data collection, online imitation learning is an active area of research in the case of hybrid IL-RL (Ross & Bagnell, 2014; Sun et al., 2018) and “safe” IL (Sun et al., 2017; Menda et al., 2017; Zhang & Cho, 2017).”
INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps (2019, methodology): “We extended the fast integrated learning and control framework proposed in [38] in the FT roundabout scenario.”
A survey of deep learning techniques for autonomous driving (2019, methodology): “gies for path planning and behavior arbitration. Following, we discuss two of the most representative deep learning paradigms for path planning, namely Imitation Learning (IL) (Rehder et al., 2017), (Sun et al., 2018), (Grigorescu et al., 2019) and Deep Reinforcement Learning (DRL) based planning (Y”