Abstract: How should we integrate representations from complementary sensors for autonomous driving? Geometry-based fusion has shown promise for perception (e.g. object detection, motion forecasting). However, in the context of end-to-end driving, we find that imitation learning based on existing sensor fusion methods underperforms in complex driving scenarios with a high density of dynamic agents. Therefore, we propose TransFuser, a mechanism to integrate image and LiDAR representations using self-attention. Our approach uses transformer modules at multiple resolutions to fuse perspective view and bird's eye view feature maps. We experimentally validate its efficacy on a challenging new benchmark with long routes and dense traffic, as well as the official leaderboard of the CARLA urban driving simulator. At the time of submission, TransFuser outperforms all prior work on the CARLA leaderboard in terms of driving score by a large margin. Compared to geometry-based fusion, TransFuser reduces the average collisions per kilometer by 48%.
Latex Bibtex Citation:
@article{Chitta2022PAMI,
  author = {Kashyap Chitta and Aditya Prakash and Bernhard Jaeger and Zehao Yu and Katrin Renz and Andreas Geiger},
  title = {TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving},
  journal = {Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
  year = {2023}
}

Abstract: Planning an optimal route in a complex environment requires efficient reasoning about the surrounding scene. While human drivers prioritize important objects and ignore details not relevant to the decision, learning-based planners typically extract features from dense, high-dimensional grid representations of the scene containing all vehicle and road context information. In this paper, we propose PlanT, a novel approach for planning in the context of self-driving that uses a standard transformer architecture. PlanT is based on imitation learning with a compact object-level input representation. With this representation, we demonstrate that information regarding the ego vehicle's route provides sufficient context regarding the road layout for planning. On the challenging Longest6 benchmark for CARLA, PlanT outperforms all prior methods (matching the driving score of the expert) while being 5.3x faster than equivalent pixel-based planning baselines during inference. Furthermore, we propose an evaluation protocol to quantify the ability of planners to identify relevant objects, providing insights regarding their decision making. Our results indicate that PlanT can reliably focus on the most relevant object in the scene, even when this object is geometrically distant.
Latex Bibtex Citation:
@inproceedings{Renz2022CORL,
  author = {Katrin Renz and Kashyap Chitta and Otniel-Bogdan Mercea and Almut Sophia Koepke and Zeynep Akata and Andreas Geiger},
  title = {PlanT: Explainable Planning Transformers via Object-Level Representations},
  booktitle = {Conference on Robot Learning (CoRL)},
  year = {2022}
}

Abstract: Simulators offer the possibility of safe, low-cost development of self-driving systems. However, current driving simulators exhibit naïve behavior models for background traffic. Hand-tuned scenarios are typically added during simulation to induce safety-critical situations. An alternative approach is to adversarially perturb the background traffic trajectories. In this paper, we study this approach to safety-critical driving scenario generation using the CARLA simulator. We use a kinematic bicycle model as a proxy to the simulator's true dynamics and observe that gradients through this proxy model are sufficient for optimizing the background traffic trajectories. Based on this finding, we propose KING, which generates safety-critical driving scenarios with a 20% higher success rate than black-box optimization. By solving the scenarios generated by KING using a privileged rule-based expert algorithm, we obtain training data for an imitation learning policy. After fine-tuning on this new data, we show that the policy becomes better at avoiding collisions. Importantly, our generated data leads to reduced collisions on both held-out scenarios generated via KING as well as traditional hand-crafted scenarios, demonstrating improved robustness.
Latex Bibtex Citation:
@inproceedings{Hanselmann2022ECCV,
  author = {Niklas Hanselmann and Katrin Renz and Kashyap Chitta and Apratim Bhattacharyya and Andreas Geiger},
  title = {KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradients},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year = {2022}
}