Conditional Generative Adversarial Networks for Speed Control in Trajectory Simulation

Julka, Sahib; Sowrirajan, Vishal; Schlötterer, Jörg

doi:10.48550/arxiv.2103.11471

Cited by 1 publication

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“…Other approaches based on Graph Neural Networks (GNNs) such as GOHOME [35] and LaneGCN [36] have achieved SOTA results on the most relevant benchmarks for Motion Prediction [35]- [37]. Moreover, despite GAN-based approaches [13], [30], [38], [39] provide certain control and interpretability, most competitive approaches on self-driving motion prediction benchmarks such as Argoverse [11] or Waymo [40] do not use adversarial training, where the training complexity is one the main reasons. HOME [12] SoPhie [30] Fig.…”

Section: Related Workmentioning

confidence: 99%

Exploring Map-based Features for Efficient Attention-based Vehicle Motion Prediction

Gómez-Huélamo¹,

Conde²,

Ortiz³

2022

Preprint

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Motion prediction (MP) of multiple agents is a crucial task in arbitrarily complex environments, from social robots to self-driving cars. Current approaches tackle this problem using end-to-end networks, where the input data is usually a rendered top-view of the scene and the past trajectories of all the agents; leveraging this information is a must to obtain optimal performance. In that sense, a reliable Autonomous Driving (AD) system must produce reasonable predictions on time, however, despite many of these approaches use simple ConvNets and LSTMs, models might not be efficient enough for real-time applications when using both sources of information (map and trajectory history). Moreover, the performance of these models highly depends on the amount of training data, which can be expensive (particularly the annotated HD maps). In this work, we explore how to achieve competitive performance on the Argoverse 1.0 Benchmark using efficient attention-based models, which take as input the past trajectories and map-based features from minimal map information to ensure efficient and reliable MP. These features represent interpretable information as the driveable area and plausible goal points, in opposition to black-box CNN-based methods for map processing. Our code is publicly available at https://github.com/Cram3r95/mapfe4mp.

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