Object Importance Estimation using Counterfactual Reasoning for Intelligent Driving (2312.02467v2)

Abstract: The ability to identify important objects in a complex and dynamic driving environment is essential for autonomous driving agents to make safe and efficient driving decisions. It also helps assistive driving systems decide when to alert drivers. We tackle object importance estimation in a data-driven fashion and introduce HOIST - Human-annotated Object Importance in Simulated Traffic. HOIST contains driving scenarios with human-annotated importance labels for vehicles and pedestrians. We additionally propose a novel approach that relies on counterfactual reasoning to estimate an object's importance. We generate counterfactual scenarios by modifying the motion of objects and ascribe importance based on how the modifications affect the ego vehicle's driving. Our approach outperforms strong baselines for the task of object importance estimation on HOIST. We also perform ablation studies to justify our design choices and show the significance of the different components of our proposed approach.

• The paper introduces a novel counterfactual reasoning method that alters object motions to assess their impact on driving decisions.
• It leverages the HOIST dataset with multi-modal sensor data and human-annotated labels to pinpoint key road objects.
• Results surpass baseline methods, demonstrating significant improvements in autonomous driving safety and efficiency.

Introduction to Object Importance Estimation

Assistive and autonomous driving technologies aim to improve safety and efficiency on the road by making informed driving decisions. These systems rely on the ability to understand and prioritize the various elements in their environment, but not all objects in a driving scene demand equal attention. The identification of important objects is a critical step for path planning and resource allocation, ensuring that attention is directed where it's most needed, and reducing unnecessary alerts.

Dataset and Counterfactual Reasoning Approach

To tackle this problem, a new dataset called HOIST (Human-annotated Object Importance in Simulated Traffic) has been introduced. It consists of simulated driving scenarios with human-annotated importance labels for both vehicles and pedestrians, and it addresses previous dataset limitations by incorporating multi-modal sensor data, including bird’s-eye views, LIDAR, and GPS.

Building on this dataset, a unique counterfactual reasoning approach has been proposed to determine object importance. This method involves altering the motion of objects in the simulation to create 'what if' scenarios and observing how these changes affect the autonomous agent’s driving decisions. The innovative aspect of this approach is that it doesn't just remove objects to gauge their significance; it also considers how changes in their velocity or path might affect driving safety and decisions.

Performance and Findings

The presented counterfactual reasoning approach surpasses several strong baseline methodologies when evaluated using the HOIST dataset. Factors such as removal from the scene and potential collisions due to changes in velocity are utilized to compute an object's importance score.

Significance and Future Work

The implications of this research are significant, with potential benefits for both autonomous and assistive driving systems. By prioritizing objects based on their impact on the driving scene, these systems can make more informed decisions, navigate more efficiently, and possibly prevent accidents by preemptively recognizing hazardous scenarios.

The contribution extends beyond a novel algorithm; by sharing the HOIST dataset and the source code, the research invites further development and validation from the broader community. This transparency in research fosters an environment for acceleration in the creation of more advanced autonomous driving systems.

By analyzing the failure cases of their approach, such as incorrect trajectory predictions by the autonomous agent, the researchers also provide valuable insights into where such systems might need further improvement.

The development of efficient and transparent algorithms for object importance estimation signifies a step towards safer, more reliable autonomous driving systems, capable of understanding and adapting to complex traffic scenarios.