Driver and Pedestrian Mutual Awareness for Path Prediction in Intelligent Vehicles

This thesis addresses the sensor-based perception of driver and pedestrian to improve joint path prediction of ego-vehicle and pedestrian based on mutual awareness in the domain of intelligent vehicles.<br/><br/>According to the World Health Organization (WHO), more than half of global traffic deaths are among Vulnerable Road Users (VRUs), such...

Detecting darting out pedestrians with occlusion aware sensor fusion of radar and stereo camera

Early and accurate detection of crossing pedestrians is crucial in automated driving in order to perform timely emergency manoeuvres. However, this is a difficult task in urban scenarios where pedestrians are often occluded (not visible) behind objects, e.g., other parked vehicles. We propose an occlusion aware fusion of stereo camera and...

Visual Detection and Pose Estimation of Vulnerable Road Users for Automated Driving

This thesis addresses the topic of visual person detection and pose estimation. While these tasks are relevant for a broad range of applications, this thesis focuses on the domain of intelligent vehicles in urban traffic scenes. This domain is particularly interesting due to specific challenges related to visual perception from a moving vehicle....

Driver and Pedestrian Mutual Awareness for Path Prediction and Collision Risk Estimation

We present a novel method for vehicle-pedestrian path prediction that takes into account the awareness of the driver and the pedestrian towards each other. The method jointly models the paths of vehicle and pedestrian within a single Dynamic Bayesian Network (DBN). In this DBN, sub-graphs model the environment and entity-specific context cues...

Scenario Parameter Generation Method and Scenario Representativeness Metric for Scenario-Based Assessment of Automated Vehicles

The development of assessment methods for the performance of Automated Vehicles (AVs) is essential to enable the deployment of automated driving technologies, due to the complex operational domain of AVs. One candidate is scenario-based assessment, in which test cases are derived from real-world road traffic scenarios obtained from driving...

Prediction-Based Reachability Analysis for Collision Risk Assessment on Highways

Real-time safety systems are crucial components of intelligent vehicles. This paper introduces a prediction-based collision risk assessment approach on highways. Given a point mass vehicle dynamics system, a stochastic forward reachable set considering two-dimensional motion with vehicle state probability distributions is firstly established. We...

3DOP: Comfort-oriented Motion Planning for Automated Vehicles with Active Suspensions

Motion comfort is the basis of many societal benefits promised by automated driving and motion planning is primarily responsible for this. By planning the spatial trajectory and the velocity profile, motion planners can significantly enhance motion comfort, ideally without sacrificing time efficiency. Active suspensions can push the boundary...

Drift Reduction for Monocular Visual Odometry of Intelligent Vehicles Using Feedforward Neural Networks

In this paper, an approach for reducing the drift in monocular visual odometry algorithms is proposed based on a feedforward neural network. A visual odometry algorithm computes the incremental motion of the vehicle between the successive camera frames, then integrates these increments to determine the pose of the vehicle. The proposed neural...

A Two-Stage Bayesian optimisation for Automatic Tuning of an Unscented Kalman Filter for Vehicle Sideslip Angle Estimation

This paper presents a novel methodology to auto-tune an Unscented Kalman Filter (UKF). It involves using a Two-Stage Bayesian Optimisation (TSBO), based on a t-Student Process to optimise the process noise parameters of a UKF for vehicle sideslip angle estimation. Our method minimises performance metrics, given by the average sum of the states’...

Crafted vs. Learned Representations in Predictive Models - A Case Study on Cyclist Path Prediction

This paper compares two models for context-based path prediction of objects with switching dynamics: a Dynamic Bayesian Network (DBN) and a Recurrent Neural Network (RNN). These models are instances of two larger model categories, distinguished by whether expert knowledge is explicitly crafted into the state representation (and thus is...

Risk Quantification for Automated Driving Systems in Real-World Driving Scenarios

The development of safety validation methods is essential for the safe deployment and operation of Automated Driving Systems (ADSs). One of the goals of safety validation is to prospectively evaluate the risk of an ADS dealing with real-world traffic. ISO 26262 and ISO/DIS 21448, the leading standards in automotive safety, provide an approach...

A Joint Extrinsic Calibration Tool for Radar, Camera and Lidar

We address joint extrinsic calibration of lidar, camera and radar sensors. To simplify calibration, we propose a single calibration target design for all three modalities, and implement our approach in an open-source tool with bindings to Robot Operating System (ROS). Our tool features three optimization configurations, namely using error...

Context-based path prediction for targets with switching dynamics

Anticipating future situations from streaming sensor data is a key perception challenge for mobile robotics and automated vehicles. We address the problem of predicting the path of objects with multiple dynamic modes. The dynamics of such targets can be described by a Switching Linear Dynamical System (SLDS). However, predictions from this...

Design analysis of a decentralized equilibrium-routing strategy for intelligent vehicles

Intelligent vehicle technologies are opening new possibilities for decentralized vehicle routing systems, suitable for regulating large traffic networks, and at the same time, capable of providing customized advice to individual vehicles. In this study, we perform a rigorous simulation-based analysis of an in-vehicle routing strategy that...

Driving characteristics and adaptive cruise control: A naturalistic driving study

With the increasing number of vehicles equipped with Adaptive Cruise Control (ACC), it becomes important to assess its impact on traffic flow efficiency, in particular with respect to capacity and queue discharge rate. Simulation studies and surveys suggest that ACC has both positive and negative effects on traffic flow, but empirical...

Optimal lane change times and accelerations of autonomous and connected vehicles

This contribution puts forward a flexible approach to model the decision-making or design controller for automated driving systems, where tactical-level lane change decisions and control-level accelerations are jointly evaluated based on iteratively solving an online optimization problem. The key idea is that automated vehicles determine lane...