Resilient Multi-range Radar Detection System for Autonomous Vehicles: A New Statistical Method

© 2023 Crown. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Critical issues with current detection systems are their susceptibility to adverse weather conditions and constraint on the vertical field view of the radars limiting the ability of such systems to accurately detect the height of the targets. In this paper, a novel multi-range radar (MRR) arrangement (i.e. triple: long-range, medium-range, and short-range radars) based on the sensor fusion technique is investigated that can detect objects of different sizes in a level 2 advanced driver-assistance system. To improve the accuracy of the detection system, the resilience of the MRR approach is investigated using the Monte Carlo (MC) method for the first time. By adopting MC framework, this study shows that only a handful of fine-scaled computations are required to accurately predict statistics of the radar detection failure, compared to many expensive trials. The results presented huge computational gains for such a complex problem. The MRR approach improved the detection reliability with an increased mean detection distance (4.9% over medium range and 13% over long range radar) and reduced standard deviation over existing methods (30% over medium range and 15% over long-range radar). This will help establishing a new path toward faster and cheaper development of modern vehicle detection systems.Peer reviewe

High-Level Interpretation of Urban Road Maps Fusing Deep Learning-Based Pixelwise Scene Segmentation and Digital Navigation Maps

This paper addresses the problem of high-level road modeling for urban environments. Current approaches are based on geometric models that fit well to the road shape for narrow roads. However, urban environments are more complex and those models are not suitable for inner city intersections or other urban situations. The approach presented in this paper generates a model based on the information provided by a digital navigation map and a vision-based sensing module. On the one hand, the digital map includes data about the road type (residential, highway, intersection, etc.), road shape, number of lanes, and other context information such as vegetation areas, parking slots, and railways. On the other hand, the sensing module provides a pixelwise segmentation of the road using a ResNet-101 CNN with random data augmentation, as well as other hand-crafted features such as curbs, road markings, and vegetation. The high-level interpretation module is designed to learn the best set of parameters of a function that maps all the available features to the actual parametric model of the urban road, using a weighted F-score as a cost function to be optimized. We show that the presented approach eases the maintenance of digital maps using crowd-sourcing, due to the small number of data to send, and adds important context information to traditional road detection systems

A Sensor for Urban Driving Assistance Systems Based on Dense Stereovision

Advanced driving assistance systems (ADAS) form a complex multidisciplinary research field, aimed at improving traffic efficiency and safety. A realistic analysis of the requirements and of the possibilities of the traffic environment leads to the establishment of several goals for traffic assistance, to be implemented in the near future (ADASE, INVENT

Predictive energy-efficient motion trajectory optimization of electric vehicles

This work uses a combination of existing and novel methods to optimize the motion trajectory of an electric vehicle in order to improve the energy efficiency and other criteria for a predefined route. The optimization uses a single combined cost function incorporating energy efficiency, travel safety, physical feasibility, and other criteria. Another focus is the optimal behavior beyond the regular optimization horizon

Development of an Autonomous Vehicle Platform

Autonomous vehicles and their related development are gaining a lot of traction as a promising up and coming technology. The Mechatronics Vehicle Systems lab at the University of Waterloo is well pioneered in the automotive industry and seeks to apply their knowledge and skills to autonomous vehicles. Having an autonomous vehicle development platform at the University allows for development and testing of state of the art algorithms that can potentially benefit the entire automotive industry. An autonomous driving platform based on a Chevrolet Equinox is proposed in this thesis. Various types of sensors are installed on the vehicle and interfaced, allowing for full coverage of the surrounding environment. A software platform is developed which uses ROS and Matlab simultaneously, benefiting from the libraries, tools, and resources that come with both. The hardware platform is designed with simplicity and functionality in mind. Moreover, a simulation platform is used for testing various algorithms before real world implementation. Various types of sensor calibrations are necessary to fully synchronize all the sensors on the platform spatially. A joint calibration method that allows for the simultaneous calibration of all 3D sensors sharing a common field of view is implemented. Specialized hand-eye calibration methods to calibrate the GPS navigation system to the LIDAR and camera sensors are explored. Furthermore, vehicle to everything interfacing is kept in mind and a calibration technique is presented in order to localize infrastructure mounted sensors to a GPS navigation system. The calibration techniques are tested and areas of improvement are revealed. The developed platform is tested with the task of autonomous lane keeping. The steering wheel angle of the vehicle is controlled by the developed algorithm utilizing the camera and GPS navigation solution. The algorithm is tested in simulation with good results. Before real world testing, time synchronization between various devices on the platform, as well as testing of the actuators' controllers is performed. Finally, the lane keeping algorithm is tested on the developed platform on the University of Waterloo Ring Road. The system is able to autonomously steer around the majority of the road which is approximately a 2.5 km distance