Master's Thesis: Function-Aware Target Confidence Estimation for Fused ADAS Sensors

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Background 

Advanced Driver Assistance Systems (ADAS) rely on sensor fusion to detect and track surrounding traffic participants. A fused system may combine camera, radar, and other sensor inputs, but the reliability of a target estimate depends on more than the confidence reported by any individual sensor. Sensor errors can be correlated, and performance can vary with distance, weather, occlusion, road scene, object type, and other operating conditions. Different vehicle functions also have different needs. Adaptive Cruise Control (ACC) should provide useful, dependable operation across its intended operating domain. Emergency braking, by contrast, must avoid unnecessary interventions while still responding to genuine collision threats. A single generic target-confidence score may therefore be unsuitable for both functions. Real-traffic logs and reference sensor data offer an opportunity to study how target confidence can be estimated and calibrated under practical conditions. Alongside conventional probabilistic methods, AI-based approaches can be evaluated in two forms: higher-capacity models for offline analysis, and smaller models with potential to run within the computational constraints of an ADAS system. 

Description of the Thesis Work 

The thesis will investigate how to estimate the confidence of fused traffic targets using reference data collected in real traffic. The work will distinguish the quality of the target estimate from the decisions made by individual ADAS functions. The study may consider confidence in several aspects of a target, such as: 

Whether a real object exists at the estimated location 

The probability of the object’s class 

The accuracy and uncertainty of position and velocity 

Track stability over time and the likelihood of a false or lost track 

The student will develop and compare a transparent probabilistic baseline with suitable AI-based methods. The analysis will account for the fact that sensor errors may share common causes, rather than assuming that camera and radar false detections are independent. The resulting confidence estimates will be assessed in the context of ACC and emergency braking. The thesis will investigate how each function’s requirements affect the interpretation and use of confidence. It will not assume that one confidence threshold is appropriate for both functions, nor that a confidence score alone is sufficient to establish system safety. 

Potential Research Questions 

How well can target existence and target-state confidence be estimated from real-traffic reference data? 

How do correlated false detections and operating conditions affect fused confidence estimates? 

How does a calibrated confidence estimate support different objectives for ACC and emergency braking? 

How do probabilistic and AI-based methods compare in calibration, robustness, and data efficiency? 

What accuracy and runtime trade-offs are achievable with a compact model suitable for constrained ADAS hardware? 

Potential Areas of Work 

Preparing and aligning logged sensor data with reference measurements and ground-truth labels 

Defining target-confidence outputs and their intended interpretation 

Estimating single-sensor and joint sensor error characteristics 

Developing a probabilistic baseline and comparing it with AI-based methods 

Calibrating confidence estimates and evaluating generalization to unseen drives or conditions 

Exploring model simplification or compression for a real-time implementation 

Assessing function-relevant performance, including ACC availability and emergency-braking false and missed intervention indicators 

Reporting limitations in rare-event estimation and reference-data coverage 

Methodology and Evaluation 

The work may compare a conventional probabilistic approach with selected machine-learning methods. Depending on data and scope, the AI methods could include a higher-capacity offline model and a smaller model intended for later real-time evaluation. Both should be assessed for probability calibration, not only their ability to rank detections. Data should be divided by drive, route, or recording session where possible, so that closely related frames do not appear in both training and test sets. Evaluation should be broken down by relevant conditions, such as range, object class, visibility, occlusion, and sensor combination. Potential evaluation measures include: 

Confidence quality: reliability plots, Brier score, and log loss 

Target performance: false and missed detections, tracking quality, and state-estimation error 

ACC relevance: availability within the defined operating domain, alongside target-estimation performance 

Emergency-braking relevance: false and missed or late intervention indicators for defined threat scenarios 

Deployment feasibility: inference time and model size, with other resource measures where available 

Rare-event rates should be reported with appropriate uncertainty and exposure definitions. For example, a false-intervention rate per driving hour is not interchangeable with a missed-intervention rate per relevant collision-threat encounter. A limited number of observed events should not be presented as proof of a very low failure rate. 

Expected Outcomes 

The thesis is expected to deliver: 

A documented method for estimating target confidence from reference data 

A comparison of probabilistic and AI-based approaches 

An analysis of sensor-error dependence and its impact on fusion 

An evaluation of confidence calibration across operating conditions 

A discussion of how confidence can be used differently by ACC and emergency braking 

Recommendations for further validation and possible real-time implementation 

Suitable Background for the Student 

We are looking for a master’s student with an interest in sensor fusion, ADAS, and data-driven system evaluation. Relevant academic backgrounds include: 

Machine Learning and Data Science 

Robotics and Sensor Fusion 

Signal Processing 

Computer Vision 

Automotive or Control Engineering 

Experience with Python, probabilistic modelling, machine learning, or automotive sensor data is beneficial. Experience with real-time or embedded machine learning is useful for the model-deployment extension. 

Practical Information 

Thesis level: Master’s thesis 

Language: English 

Starting date:Jan 2027

Application deadline: 30th Nov

The final scope will depend on the available logged data, reference labels, and access to function-level evaluation criteria. A practical thesis can focus on confidence estimation and offline evaluation, with real-time model assessment as an extension if resources permit. 

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