LiDAR Echo Emulation Using 3D Ray Tracing for Moving Targets
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Solution Overview
Problem
Conventional advanced driver-assistance systems (ADASs) and autonomous driving systems face challenges in accurately interpreting echo signals from LiDAR sensors due to complex driving environments and vehicle movement, leading to potential false warnings or collisions.
Innovation Solution
A method and system for emulating echo signals using a 3D simulation scene with dynamic models, ray tracing, and graphical processor units (GPUs) to simulate the physical behavior of LiDAR signals, accounting for vehicle velocity-dependent distortions and motion blur, allowing for real-time simulation of LiDAR responses without actual vehicle testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LiDAR sensor is mounted on a moving vehicle to perform real-world testing, then measurement accuracy and reliability can be improved, but test time and risk increase due to complex driving environments and vehicle movement distortions
Solution Approach 1:
The patent creates a virtual copy of the real-world driving environment through a 3D simulation scene that replicates complex driving conditions, objects, and scenarios. This virtual copy allows testing without physical vehicle deployment, eliminating the need for time-consuming real-world tests while maintaining measurement accuracy through realistic simulation of LiDAR signal interactions.
Solution Approach 2:
The system performs preliminary actions by pre-building detailed 3D simulation scenes with accurate geometric models, material properties, and environmental conditions before testing. Ray tracing calculations are pre-computed for various scenarios, allowing rapid virtual testing without requiring actual vehicle deployment in complex driving environments.
2Adaptability or versatility
If LiDAR sensor is mounted on a moving vehicle to capture real driving scenarios, then adaptability to complex environments can be improved, but measurement distortion increases due to vehicle velocity-dependent effects
Solution Approach 1:
The system copies realistic vehicle movement patterns and environmental conditions into the virtual simulation, allowing testing of adaptability to diverse scenarios including rain, fog, night, and complex urban environments without suffering from actual vehicle movement distortions. The simulation accurately replicates how LiDAR signals interact with different surfaces and weather conditions.
Solution Approach 2:
The patent introduces ray tracing as an intermediary computational method that accurately models LiDAR signal propagation through complex environments. This intermediary calculation layer compensates for vehicle movement effects by computing precise time-of-flight measurements that account for relative motion between the virtual LiDAR sensor and simulated targets, eliminating velocity-dependent measurement errors.
3Reliability
If conventional testing methods are used with actual vehicle deployment, then measurement reliability can be improved, but device complexity and test cost increase
Solution Approach 1:
The patent replaces complex physical testing infrastructure with a virtual copy of the testing environment. The 3D simulation scene with ray-traced lighting and material properties replicates real-world optical interactions, allowing reliable detection testing without the complexity of coordinating actual vehicle deployments, multiple physical sensors, and real-world safety protocols.
4Productivity
If real-time simulation of LiDAR responses is implemented, then productivity can be improved, but computational complexity increases due to ray tracing requirements
Solution Approach 1:
The system performs preliminary computations by pre-calculating ray tracing results for various scenarios, pre-building acceleration structures for the 3D simulation scene, and pre-computing light paths through complex environments. This preliminary preparation enables rapid real-time querying of LiDAR responses without performing full ray tracing during actual testing, maintaining high productivity while managing computational complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate and cost-effective emulation of LiDAR responses, reducing test time and risk, while simulating complex driving scenarios with minimal latency and high performance, effectively addressing the distortions caused by vehicle movement.
Implementation Method 1
The light pulses illuminate a spinning mirror, for example, that redirects the light in different azimuth angles from the sensor
Implementation Method 2
The time delay of the round-trip pulse received at the LiDAR receiver is used to determine the distance from the object
Implementation Method 3
The optical front end includes an optical collection lens configured to receive the LiDAR signal transmitted by the LiDAR sensor and an optical transmitter lens configured to transmit the emulated echo signal responsive to the LiDAR signal reflecting from the moving emulated target
Data Source
AI summary
A system and method are provided for emulating echo signals in response to a LiDAR signal. The method includes updating a current position of a moving emulated target according to a 3D simulation scene at a current frame, the simulation scene including a dynamic model of the target; estimating a next position of the target at a next frame of the simulation scene by updating motion transforms for the dynamic model using motion keys; performing ray tracing by launching rays in parallel, assigning different pulse times to the rays to simulate timing of corresponding light pulses of the LiDAR signal, estimating positions of the target at the different pulse times using interpolation, and identifying intersections of the rays with the estimated positions of as positions of hits of the rays; transmitting emulated echo signals to the LiDAR sensor indicating the positions of the hits; and updating the simulation scene.