QML for Argoverse 2 Motion Forecasting Challenge
This work addresses the need for efficient and accurate motion forecasting in autonomous driving systems, but it appears incremental as it builds on existing challenges without introducing a new paradigm.
The authors tackled the problem of motion forecasting for autonomous driving by developing a solution that balances accuracy and latency, achieving 3rd place in the Argoverse 2 Motion Forecasting Challenge 2022.
To safely navigate in various complex traffic scenarios, autonomous driving systems are generally equipped with a motion forecasting module to provide vital information for the downstream planning module. For the real-world onboard applications, both accuracy and latency of a motion forecasting model are essential. In this report, we present an effective and efficient solution, which ranks the 3rd place in the Argoverse 2 Motion Forecasting Challenge 2022.