Dynamic Environment Perception and Object Tracking in Autonomous Systems
Summary
Autonomous platforms—from self-driving cars to unmanned aerial vehicles—rely on robust perception systems to interpret and navigate complex, ever-changing surroundings. Central to this endeavour is the ability to distinguish static from dynamic elements, construct coherent spatial representations and maintain continuous object trajectories in real time. Advances in sensor technology (notably LiDAR, radar and vision), probabilistic mapping and multi-sensor fusion have together driven improvements in environment modelling, enabling vehicles to predict object motion, plan safe manoeuvres and adapt to unexpected events. Core challenges include coping with sensor noise, occlusions and limited range, while balancing computational demands on embedded hardware. Recent studies have emphasised joint modelling of static and moving occupancy, hybrid kinematic estimation and efficient data association to deliver reliable tracking in cluttered urban and industrial scenarios. The convergence of mapping, detection and temporal filtering techniques is paving the way for ever more capable autonomous systems with broad applications in transportation, logistics and public safety.
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Dynamic Environment Perception and Object Tracking in Autonomous Systems publication trend
The graph below shows the total number of articles in dynamic environment perception and object tracking in autonomous systems across all publications each year (not limited to Nature Index journals).
Technical terms
Occupancy grid: A spatial tessellation that represents the probability of each cell being occupied by an object or free space.
Point cloud: A collection of spatial data points captured by range sensors, representing object surfaces in three dimensions.
Sensor fusion: The process of integrating data from multiple sensor modalities to produce a more accurate, unified perception of the environment.
Extended Kalman Filter (EKF): A nonlinear state estimation algorithm that linearises motion and measurement models around current estimates to track dynamic objects.
Bayesian network: A graphical model that encodes probabilistic relationships among variables, used to infer occupancy and motion states.
Data association: Techniques for matching sensor measurements to existing object tracks, crucial for maintaining consistent identities over time.
References
- Transitional Grid Maps: Joint Modeling of Static and Dynamic Occupancy. IEEE Open Journal of Intelligent Transportation Systems (2024).
- Application of Data Sensor Fusion Using Extended Kalman Filter Algorithm for Identification and Tracking of Moving Targets from LiDAR–Radar Data. Remote Sensing (2023).
- LiDAR-Based Dense Pedestrian Detection and Tracking. Applied Sciences (2022).
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