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.
In this paper, we explore the potential of using a large language model (LLM) to understand the driving environment in a human-like manner and analyze its ability to reason, interpret, and memorize when facing complex scenarios.
Engineering 5.0 · Research 7.5 · Business 5.0
This paper presents the implementation of a Deep Reinforcement Learning based algorithm for the control of a lane following car.
Engineering 5.0 · Research 7.0 · Business 5.0
This technical report summarizes the winning solution for the 3D Occupancy Prediction Challenge, which is held in conjunction with the CVPR 2023 Workshop on End-to-End Autonomous Driving and CVPR 23 Workshop on Vision-Centric Autonomous Driving Workshop.
Engineering 7.5 · Research 8.0 · Business 5.0
The autonomous driving community has witnessed a rapid growth in approaches that embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle motion plans, instead of concentrating on individual tasks such as detection and motion prediction.
Engineering 5.5 · Research 7.5 · Business 5.0
The solution to the problem of road environmental perception is one of the essential prerequisites to realizing the autonomous driving of intelligent vehicles, and road lane detection plays a crucial role in road environmental perception.
Engineering 5.0 · Research 7.0 · Business 6.0
Nowadays, autonomous vehicles are gaining traction due to their numerous potential applications in resolving a variety of other real-world challenges.
Engineering 5.5 · Research 7.0 · Business 5.5
An online trajectory planning method for collision avoidance is proposed to improve vehicle driving safety and comfort simultaneously.
Engineering 5.0 · Research 8.0 · Business 5.0
An autonomous driving research paper: F-3DNet: Extracting inner order of point cloud for 3D object detection in autonomous driving.
Engineering 5.0 · Research 7.0 · Business 5.0
To this end, we find that appropriately limiting attention spans and modeling information at different granularity can introduce local constraints and enhance attention representations.
Engineering 5.0 · Research 8.0 · Business 5.0
In this work, we propose a multi-modal fusion 3D object detection model for autonomous driving to use the best out of LiDAR and camera sensors.
Engineering 5.5 · Research 7.0 · Business 5.0
Perception, computing and communication are usually decoupled in today’s vehicle-road coordination applications, which significantly adds to the system delay and cost.
Engineering 5.0 · Research 7.0 · Business 5.0
We present a novel hybrid trajectory optimization method aimed at generating efficient, stable, and smooth traversal motions.
Engineering 5.5 · Research 8.0 · Business 6.5
In this study, aimed at the test scenarios of autonomous vehicle, we propose a lidar-camera fusion approach for traffic environment sensing.
Engineering 5.0 · Research 7.0 · Business 5.0
Lane detection is an essential task in autonomous driving.
Engineering 5.0 · Research 8.0 · Business 5.0
In this paper, we propose an end-to-end transformer-based MOT algorithm (MotionTrack) with multi-modality sensor inputs to track objects with multiple classes.
Engineering 6.0 · Research 7.0 · Business 5.0
This paper presents CarFASE, an open-source carla-based fault and attack simulation engine that is used to test and evaluate the behavior of autonomous driving stacks in the presence of faults and attacks.
Engineering 6.5 · Research 7.0 · Business 5.0
In this paper, an efficient LiDAR point cloud object detection, based on the YoloV4 model, has been proposed, which gives high detection accuracy with the ability to process more than 27 frames per second.
Engineering 5.0 · Research 8.0 · Business 5.5
In this work, we present a novel USS-based object detection system that can enable accurate detection of objects in low-speed scenarios.
Engineering 5.0 · Research 8.0 · Business 5.0
Our method avoids unnecessary computation, as it can be directly queried by the motion planner at continuous spatio-temporal locations.
Engineering 5.0 · Research 8.0 · Business 5.0
The results of defined distance metrics on the KITTI dataset show that our approach is highly competitive with existing models and outperforms current state-of-the-art approaches that only use the detected vehicle’s height to determine depth.
Engineering 5.5 · Research 8.0 · Business 6.0