Browse curated autonomous driving papers on end-to-end driving, BEV perception, 3D object detection, motion prediction, path planning, ADAS, Tesla FSD, Waymo, and self-driving foundation models.
This paper presents a new approach for enhancing autonomous vehicle navigation and obstacle avoidance based on the integration of reinforcement learning with multiple sensors for navigation.
Engineering 5.0 · Research 7.0 · Business 5.0
Effective autonomous driving systems require a delicate balance of high precision, efficient design, and immediate response capabilities.
Engineering 6.0 · Research 8.0 · Business 5.5
To overcome these constraints, we present a novel three-stage vision-based pipeline.
Engineering 5.0 · Research 8.0 · Business 5.0
In this paper, we propose SCNet3D, a novel pillar-based method that tackles the challenges of feature enhancement, information preservation, and small target detection from the perspectives of features and data.
Engineering 5.0 · Research 8.0 · Business 5.0
In recent years, advancements in light laser detection and ranging (LiDAR) and camera technologies have brought increasing attention to autonomous driving.
Engineering 8.0 · Research 7.0 · Business 5.5
This paper mainly discusses about automation.
Engineering 5.5 · Research 7.0 · Business 6.0
In this paper, we address multi-sensor fusion in AVs and present a comprehensive integration of a thermal sensor aimed at enhancing the cognitive robustness of AVs.
Engineering 5.0 · Research 8.0 · Business 5.0
To address these challenges, this paper presents a Vehicle-to-Infrastructure Multi-Sensor Fusion (V2I-MSF) framework that integrates LiDAR and camera data from vehicle OBUs with sensor data from roadside infrastructure.
Engineering 5.0 · Research 7.0 · Business 5.0
This paper proposes a path-planning approach based on deep reinforcement learning for a quadrotor equipped with only a monocular camera.
Engineering 5.0 · Research 7.0 · Business 5.0
: At present, energy consumption is one of the main bottlenecks in autonomous mobile robot development.
Engineering 5.0 · Research 7.0 · Business 5.0
Autonomous racing requires fast, anticipatory, and interaction-aware trajectory planning, where the ego vehicle must interpret complex track geometries while reasoning about the future behavior of multiple competitive agents.
Engineering 5.0 · Research 7.0 · Business 5.0
Large-scale camera localization in driving environments is pivotal in autonomous driving and robotics, where deep learning-based solutions offer promise for end-to-end problem-solving.
Engineering 5.5 · Research 8.0 · Business 5.0
An autonomous driving research paper: From Road to Code: Neuro-Symbolic Program Synthesis for Autonomous Driving Scene Translation and Analysis.
Engineering 5.0 · Research 7.0 · Business 5.0
An autonomous driving research paper: System-level simulation-based verification of Autonomous Driving Systems with the VIVAS framework and CARLA simulator.
Engineering 5.0 · Research 7.0 · Business 5.0
To overcome these limitations, we propose a scene-centric transformer architecture with a cluster-based training approach, capturing pedestrian dynamics through combined probability distributions.
Engineering 5.5 · Research 8.0 · Business 5.5
To address this limitation, this paper proposes a bidirectional interaction network (DyMap) that takes into account the bilateral constraints of the map on the agents and the impact of the agents on the map, using a unique bidirectional attention module.
Engineering 5.0 · Research 8.0 · Business 5.0
An autonomous driving research paper: 3D Semantic Occupancy Prediction for Autonomous Driving Using Improved GaussionFormer Models.
Engineering 5.0 · Research 7.0 · Business 5.0
In this paper, we propose a lightweight two‐branch binarization network, TBBOcc, to break through the bottleneck of ‘efficiency‐accuracy’ trade‐off through multi‐technology co‐optimization.
Engineering 6.0 · Research 7.0 · Business 6.0
An autonomous driving research paper: A Synthetic Benchmark for Collaborative 3D Semantic Occupancy Prediction in V2X Autonomous Driving.
Engineering 5.0 · Research 7.0 · Business 5.0
In this paper, we find that the complementary roles of point clouds and images vary with depth.
Engineering 5.0 · Research 7.0 · Business 5.0