Divide-and-Conquer for Lane-Aware Diverse Trajectory Prediction (2104.08277v1)

Abstract: Trajectory prediction is a safety-critical tool for autonomous vehicles to plan and execute actions. Our work addresses two key challenges in trajectory prediction, learning multimodal outputs, and better predictions by imposing constraints using driving knowledge. Recent methods have achieved strong performances using Multi-Choice Learning objectives like winner-takes-all (WTA) or best-of-many. But the impact of those methods in learning diverse hypotheses is under-studied as such objectives highly depend on their initialization for diversity. As our first contribution, we propose a novel Divide-And-Conquer (DAC) approach that acts as a better initialization technique to WTA objective, resulting in diverse outputs without any spurious modes. Our second contribution is a novel trajectory prediction framework called ALAN that uses existing lane centerlines as anchors to provide trajectories constrained to the input lanes. Our framework provides multi-agent trajectory outputs in a forward pass by capturing interactions through hypercolumn descriptors and incorporating scene information in the form of rasterized images and per-agent lane anchors. Experiments on synthetic and real data show that the proposed DAC captures the data distribution better compare to other WTA family of objectives. Further, we show that our ALAN approach provides on par or better performance with SOTA methods evaluated on Nuscenes urban driving benchmark.

• The paper introduces the Divide-and-Conquer (DAC) method and the ALAN framework to improve multimodal, lane-aware trajectory prediction for autonomous vehicles.
• The DAC method enhances multimodal learning by incrementally partitioning data to eliminate spurious modes and better capture distribution, outperforming traditional MCL objectives.
• The ALAN framework uses lane centerlines as anchors for semantically aligned trajectory prediction, converting it to a regression problem in normal-tangential coordinates to reduce errors and off-road rates.

Overview of Lane-Aware Diverse Trajectory Prediction

The paper presents an advanced approach to the critical task of trajectory prediction for autonomous vehicles (AVs), focusing on improving multimodal outputs and constraining predictions with explicit lane awareness. The authors introduce two key contributions: the Divide-and-Conquer (DAC) method for optimizing multimodal learning and a novel trajectory framework called ALAN. Both aim to enhance the prediction accuracy and safety of AVs by delivering diverse and semantically aligned trajectories.

Divide-and-Conquer Methodology

The DAC approach addresses limitations in current Multimodal Choice Learning (MCL) objectives such as Winner-Takes-All (WTA) and Best-of-Many. These traditional methods are plagued by initialization sensitivity, often leading to spurious output modes that can compromise prediction diversity. DAC circumvents this by incrementally increasing the effective number of hypothesis outputs during training. This dynamic partitioning ensures that each hypothesis captures a valid segment of the data distribution, eliminating spurious modes and facilitating convergence to optimal hypotheses. As evidenced in experiments, DAC enhances the capture of data distributions in both synthetic and real datasets, outperforming conventional methods like EWTA and RWTA.

ALAN Framework for Trajectory Prediction

The ALAN (Anchor-based Lane-aware Accountability Network) framework forms the second major contribution. It leverages existing lane centerlines to act as reference anchors, ensuring trajectory predictions adhere to realistic lane constraints. ALAN transforms trajectory prediction into a regression problem over normal-tangential coordinates along lane centerlines, providing paths that are semantically aligned with road structures. Coupled with hypercolumn descriptors, the network can incorporate interactions between multiple agents, delivering precise trajectories with low Final Displacement Error (FDE) and low OffRoadRate.

Implications and Future Directions

The proposed methodologies have significant implications for the AV industry. First, DAC enhances robustness in stochastic learning tasks by better balancing multimodal outputs. Second, ALAN's anchor-based system offers a sustainable approach to embedding driving knowledge into machine learning processes, crucial for safety in urban environments.

For practical applications, the methods support increased AV reliability and route adherence, mitigating risks in crowded or complex intersection scenarios. Theoretically, further exploration into DAC could extend to other domains requiring stochastic outcome predictions, fostering developments in varied fields like robotic navigation, activity recognition, and scene understanding.

Looking ahead, integrating DAC and ALAN in broader AI applications could involve developing hybrid models that merge trajectory outputs with real-time sensing and decision frameworks. This would offer a comprehensive approach to dynamic route planning in evolving driving conditions. Furthermore, ensuring DAC's adaptability to models other than MCL can open paths for innovation across diverse machine learning challenges. The paper sets a foundation for enhancing prediction systems in AVs, crucial for their deployment in real-world environments.