The field of autonomous driving is rapidly advancing, with a focus on improving perception, planning, and control in complex environments. Recent developments have centered around enhancing sensor capabilities, such as LiDAR and camera systems, to provide more accurate and robust data for autonomous vehicles. Additionally, researchers have been exploring innovative methods for sensor synchronization, including wireless time synchronization and millisecond-accurate temporal synchronization, to enable seamless communication between vehicles and infrastructure. Noteworthy papers in this area include CATS-V2V, which introduces a real-world dataset for vehicle-to-vehicle cooperative perception, and LiSTAR, which presents a novel generative world model for 4D LiDAR sequences. These advancements have the potential to significantly improve the safety and efficiency of autonomous driving systems.
Advancements in Autonomous Driving and Sensor Synchronization
Sources
Simulating an Autonomous System in CARLA using ROS 2
Miniature Testbed for Validating Multi-Agent Cooperative Autonomous Driving
CATS-V2V: A Real-World Vehicle-to-Vehicle Cooperative Perception Dataset with Complex Adverse Traffic Scenarios
One target to align them all: LiDAR, RGB and event cameras extrinsic calibration for Autonomous Driving
CorrectAD: A Self-Correcting Agentic System to Improve End-to-end Planning in Autonomous Driving
Boosting performance: Gradient Clock Synchronisation with two-way measured links
nuCarla: A nuScenes-Style Bird's-Eye View Perception Dataset for CARLA Simulation
PAVE: An End-to-End Dataset for Production Autonomous Vehicle Evaluation
V2VLoc: Robust GNSS-Free Collaborative Perception via LiDAR Localization
Benchmarking OpenWiFiSync on ESP32: Towards Cost-Effective Wireless Time Synchronization
Cracking the Microsecond: An Efficient and Precise Time Synchronization Scheme for Hybrid 5G-TSN Networks
RocSync: Millisecond-Accurate Temporal Synchronization for Heterogeneous Camera Systems
LiSTAR: Ray-Centric World Models for 4D LiDAR Sequences in Autonomous Driving