ADAS Trajectory Detection Using Multi-Sensor Fusion
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Solution Overview
Problem
Current advanced driver assistance systems (ADAS) lack effective methods to detect and respond to anomalous vehicle trajectories, particularly those influenced by driver fatigue or adverse conditions, which can lead to hazardous situations on the road.
Innovation Solution
A system utilizing ADAS camera sensors, radar, and lidar sensors, combined with V2X communications, to detect and analyze vehicle trajectories, identify anomalous patterns, and alert drivers or adjust vehicle paths to prevent collisions, by comparing detected trajectories with predetermined data and triggering alerts or corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ADAS sensors (camera, radar, lidar) are used to detect vehicle trajectories, then measurement precision of vehicle position and speed is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple ADAS sensors (camera, radar, lidar) into an integrated sensor system that shares common processing resources and data fusion algorithms. This merging approach enables precise trajectory detection through multi-sensor data correlation while reducing overall system complexity by eliminating redundant processing units and enabling shared computational infrastructure.
2Reliability
If multiple ADAS sensors are integrated to detect anomalous trajectories, then reliability of hazard detection is improved, but device complexity increases
Solution Approach 1:
The system implements feedback mechanisms where detection results from multiple sensors are continuously correlated and validated against each other. The processing unit uses feedback loops to cross-check trajectory data from cameras, radar, and lidar, adjusting detection parameters based on sensor performance and environmental conditions. This feedback-driven approach enhances detection reliability while managing complexity through adaptive parameter adjustment rather than fixed complex processing.
Solution Approach 2:
The processing unit is designed with multi-functionality to handle various sensor types and processing tasks using a unified architecture. It can perform trajectory detection, anomaly recognition, and hazard classification using the same core processing resources, enabling reliable multi-sensor integration without proportionally increasing system complexity through specialized dedicated circuits for each function.
3Loss of time
If real-time trajectory analysis is performed to identify anomalous patterns, then safety response time is improved, but use of energy increases
Solution Approach 1:
The system applies partial processing action by performing full real-time trajectory analysis only when anomaly indicators are detected, while using reduced processing for normal trajectory monitoring. The processing unit continuously monitors basic trajectory parameters with minimal energy consumption and escalates to intensive analysis only when deviation from expected patterns occurs, thereby maintaining fast response times for hazards while significantly reducing average energy consumption during normal driving conditions.
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
Enhances safety by reducing collision probabilities and enabling autonomous vehicles to alert drivers about potentially hazardous situations, improving safety near vehicles with fatigued or impaired drivers.
Implementation Method 1
The camera sensors of an advanced driver assistance system (ADAS) capture periodical images of the traffic
Implementation Method 2
ADAS radar sensors use the radio waves for measuring the distances to obstacles and to the vehicles in traffic
Implementation Method 3
Lidar sensors measure the distance to the vehicles and obstacles that reflects the laser light pulses emitted by the sensor
Data Source
AI summary
A method and device for determining an anomalous driving pattern of a neighboring vehicle using a vehicle camera and/or other sensor is described. Image data is received and if suitable lane markings are detected, a reference trajectory is derived from the detected lane markers. Otherwise, a reference trajectory is derived from a motion of the present vehicle. A trajectory of the neighboring vehicle is determined, characteristic parameters of the detected trajectory are derived, and the characteristic parameters are compared with predetermined trajectory data. Based on the comparison it is determined if the trajectory of the neighboring vehicle is an anomalous trajectory and in one case an alert signal is generated.