Interpretable Goal-based Prediction and Planning for Autonomous Driving (2002.02277v3)

Abstract: We propose an integrated prediction and planning system for autonomous driving which uses rational inverse planning to recognise the goals of other vehicles. Goal recognition informs a Monte Carlo Tree Search (MCTS) algorithm to plan optimal maneuvers for the ego vehicle. Inverse planning and MCTS utilise a shared set of defined maneuvers and macro actions to construct plans which are explainable by means of rationality principles. Evaluation in simulations of urban driving scenarios demonstrate the system's ability to robustly recognise the goals of other vehicles, enabling our vehicle to exploit non-trivial opportunities to significantly reduce driving times. In each scenario, we extract intuitive explanations for the predictions which justify the system's decisions.

• The paper presents a novel framework (IGP2) that uses rational inverse planning to infer the goals of other vehicles.
• It integrates Monte Carlo Tree Search to simulate future traffic scenarios, enabling safer and optimal planning for autonomous driving.
• Interpretable maneuver libraries provide clear, human-understandable explanations that enhance safety and debugging in urban driving conditions.

Summary of "Interpretable Goal-based Prediction and Planning for Autonomous Driving"

The paper "Interpretable Goal-based Prediction and Planning for Autonomous Driving" presents a method for improving the prediction and planning capabilities of autonomous vehicles through interpretable and goal-directed strategies. The proposed Interpretable Goal-based Prediction and Planning (IGP2) system leverages rational inverse planning and Monte Carlo Tree Search (MCTS) to achieve this integration, focusing on recognizing and interpreting the goals of other vehicles. This method stands out by emphasizing not only the predictive accuracy but also the interpretability of plans, aiming to create safer and more transparent autonomous driving systems.

Key Contributions

The IGP2 framework is specifically developed to address two significant challenges in autonomous driving: the prediction of other road users' future maneuvers and the integration of these predictions into the ego vehicle's planning process. The contributions of the paper can be broadly outlined as follows:

1. Goal Recognition via Rational Inverse Planning: The core of the IGP2 system is its ability to infer the goals of other vehicles on the road using rational inverse planning. By examining the maneuvers executed by nearby vehicles, IGP2 predicts possible future actions based on the assumption that vehicles choose maneuvers rationally to reach particular goals.
2. Efficient Planning with MCTS: The ego vehicle's planning process utilizes MCTS, informed by the predicted goals and trajectories of other vehicles. This hierarchical task planning allows the vehicle to make optimal driving decisions by simulating future traffic scenarios and selecting maneuvers that maximize a defined reward function.
3. Interpretable Maneuver Library and Macro Actions: The system employs a finite set of interpretable maneuvers and macro actions, enabling human-understandable predictions and plans. This aspect is crucial for debugging and for building trust in autonomous systems, as it provides explanations for the vehicle's behavior.
4. Simulation-Based Evaluation: The paper demonstrates the effectiveness of IGP2 through simulations in various urban driving scenarios. The results indicate that the system is capable of accurately recognizing vehicle goals, leading to significant improvements in driving efficiency, particularly in reducing travel times.

Implications and Future Directions

The IGP2 framework's emphasis on interpretability and rational planning has important implications for the development of autonomous vehicles. By providing a means to extract intuitive explanations for vehicle behavior, the system enhances transparency, which is essential for public trust and acceptance. Furthermore, the ability to understand and predict the intentions of other road users improves safety by allowing the autonomous vehicle to make proactive driving decisions.

For future work, the paper suggests addressing scenarios involving occluded objects—where vehicles may have information not available to the ego vehicle—and incorporating modeling of human irrationalities. Such extensions would significantly enhance the robustness of goal recognition and prediction capabilities.

In conclusion, the IGP2 system advances the field of autonomous driving by proposing a method that not only improves prediction and planning through goal recognition but also prioritizes interpretability, facilitating faster and safer decision-making. The paper provides a substantial contribution to the development of autonomous systems that are not only efficient but also transparent in their decision-making processes.