DSRC Versus LTE-V2X: Empirical Performance Analysis of Direct Vehicular Communication Technologies

Vehicle-to-Vehicle (V2V) communication systems have an eminence potential to improve road safety and optimize traffic flow by broadcasting Basic Safety Messages (BSMs). Dedicated Short-Range Communication (DSRC) and LTE Vehicle-to-Everything (V2X) are two candidate technologies to enable V2V communication. DSRC relies on the IEEE 802.11p standard for its PHY and MAC layer while LTE-V2X is based on 3GPP’s Release 14 and operates in a distributed manner in the absence of cellular infrastructure. There has been considerable debate over the relative advantages and disadvantages of DSRC and LTE-V2X, aiming to answer the fundamental question of which technology is most effective in real-world scenarios for various road safety and traffic efficiency applications. In this paper, we present a comprehensive survey of these two technologies (i.e., DSRC and LTE-V2X) and related works. More specifically, we study the PHY and MAC layer of both technologies in the survey study and compare the PHY layer performance using a variety of field tests. First, we provide a summary of each technology and highlight the limitations of each in supporting V2X applications. Then, we examine their performance based on different metrics

Trailer Articulation Angle Detection and Tracking For Trailer Backup Assistant Systems

SUVs and pickup trucks have a significant share of the United States market. This fact has motivated automakers to equip their products with more safety and convenience features, such as the Advanced trailer backup assistance system (TBAS). TBAS helps to take the frustration out of the driver of the tow-vehicle during the backup maneuvers. To operate an autonomous reversing maneuver, the TBAS requires the trailer-tow vehicle combined kinematic model, where a key parameter is the articulation angle between the tow vehicle and the trailer. In this dissertation, we aim to develop three models to detect the articulation angle between tow-vehicle and trailer using the rear-side camera and radar sensors. Models, each designed as an independent module for detecting the trailer articulation angle, are; computer vision-based hitch angle detection, radar-based hitch angle tracking, and deep learning-based RadarRegNet-based hitch angle estimation. The computer vision-based module estimates the relative trailer angle using a deep learning object detection model to detect marker-lights on the trailer. The proposed computer vision model processes the image frames acquired from the rear-facing camera to detect and track the trailer and estimate its orientation to the tow vehicle. The proposed radar-based hitch angle tracking, for the estimate of the hitch angle, processes reflections acquired from the mmWave radars situated at the rear side of the vehicle to detect the trailer and track its orientation in relation to the tow-vehicle. This technique is based on the tracking of individual points in the merged radars point-cloud. Each tracked point is considered as a hitch angle estimator. Using the current and past position information of a point, the model estimates the current hitch angle. Finally, the proposed RadarRegNet-based hitch angle estimation model employs a deep learning image regression convolutional neural network. The network input is an occupancy grid map of the radar points in the merged point-clouds acquired by two radars, and its output is the hitch angle. Aiming for a reasonable inference time for the time-critical hitch angle estimation task, we introduced a modified Inception block. In the proposed modified Inception block to reduce the computational cost by up to 50%

A synchronous multi-modal data acquisition and processing system for ADAS applications using an onboard GPU

We present a cost-effective testbed and advanced software architecture suitable for ADAS applications, which utilizes an onboard GPU for high-performance processing. By implementing an optimized parallel processing method, this innovative architecture can collect and synchronize multi-sensor data, such as camera and radar, with high precision. The software architecture dynamically allocates processing power to each data processing sub-module based on the utilized sensor hardware and optimizes the data flow accordingly. With the aid of parallel processing, the system can effectively reduce the time delay during data acquisition. Indoor and road stereo-camera and dual-radar data acquisition results are presented. Post-processing of the data includes the creation of disparity map depth images and combined radar point clouds identifying departing and approaching vehicles and objects

Hitch Angle Estimation for Trailer Backup System – An Object Detection and Tracking Approach

This paper proposes a novel technique for trailer angle detection (TAD) for use in an advanced trailer backup assistance system (TBAS) to accomplish a semi-autonomous or full-autonomous backup maneuver. TBAS incorporates a combined trailer-tow vehicle kinematic model that requires an accurate estimate of the hitch angle. The proposed computer vision (CV) and machine learning (ML) TAD model process the image frames, acquired from the rear-facing camera, to detect and track the trailer and estimate its orientation in relation to the tow vehicle. The technique is based on a deep learning object detection and computer vision tracking model to detect and track one or more identifiable objects on the trailer (the marker-lights on the front edges of the trailer in this work). The estimated positions of the detected marker-lights are used to perform the hitch angle estimation. The model detects the hitch angle within the specified limit with an acceptance rate of 98%. The model is implemented in real-time with a processing rate of more than 30 frames per second (fps)

Automotive Radar-Based Hitch Angle Tracking Technique for Trailer Backup Assistant Systems

We propose a technique for trailer angle detection (TAD) for use in advanced trailer backup assistance system (TBAS) for semi-autonomous or full-autonomous backup maneuver. TBAS incorporates a combined trailer-tow-vehicle kinematic model, which is requires an estimate of the hitch angle. The proposed radar-based TAD model, for the estimate of the hitch angle, processes reflections acquired from the mmWave radars situated at the rear side of the vehicle, to detect the trailer and track its orientation in relation to the tow-vehicle. This technique is based on the tracking of individual points in the merged radars point-cloud. Each tracked point is considered as a hitch angle estimator. Using the current and past position information of a point, the model estimates the current hitch angle. To offer an accurate and reliable estimation for the hitch angle, the model fuses the estimated hitch angle by all estimators, as well as the yaw rate of the vehicle. To track each individual radar point, the model employs the extended Kalman filter, which is robust to the noisy radar measurements. In the presence of strong and persistence reflections from the trailer, the model can track the trailer successfully and return the hitch angle with a reliability measure equal to 90%

Extrinsic Radar Calibration with Overlapping FoV and Hitch Ball Position Estimation

Sensor fusion, in many perception algorithms, requires detections from multiple sensors to be transformed onto a conveniently chosen coordinate system for joint processing. The position and orientation of the sensors need to be determined for the fusion procedure. Two automotive radars installed in the taillight fixtures of a truck are considered in this work and their orientation is defined with respect to the straight line connecting them. We extrinsically calibrate the radar geometry by estimating the rotation (mount angle) and translation parameters that are needed to transform the detections from the radars onto a coordinate system whose origin is at the truck\u27s hitch ball. This coordinate system is a convenient choice for algorithms which use radars, installed in similar locations, to track or sense the rotation of an attached trailer about the hitch ball. The calibration is performed by rotating a trailer or a platform, upon which corner reflectors (CRs) are placed, about the hitch ball in the direction of both the radars. The algorithm is based on two principles: 1) the use of common detections found in the overlapping field of view (FoV) of the radars to estimate the rotation parameters and 2) a search for the center of trailer/platform rotation to estimate the translation parameters, which define the hitch ball position relative to the radars. It is shown that the use of more radar detections tends to increase the calibration accuracy. The data collected in an experiment result in estimation errors of about 0.20° and 3 cm for the rotation and translation parameters, respectively

Extrinsic Calibration of Radar Mount Position and Orientation with Multiple Target Configurations

Radars are part of the sensor suite installed on modern vehicles for environmental perception. The position and orientation of the radar must be known in order to transform the detections from the radar coordinate system to a vehicle coordinate system (VCS), which is a common requirement for multi-sensor fusion. In this work, 77GHz automotive radar sensors are extrinsically calibrated by registering the radar detections of corner reflector targets with known locations of the targets in the VCS; the procedure estimates the position and orientation parameters needed to transform radar detections onto the VCS. Radar detections are noisy and very sparse, hence, effort is put into achieving good calibration accuracy by taking advantage of multiple target configurations. Two multi-target methods are discussed; one which models estimation error as white noise and averages multiple estimates, another which combines all observations to make the data points denser for a one-time global estimation. The methods are tested with both synthetic and experimental data. The synthetic data result shows that, with sparse data points per target configuration, the estimation errors obtained due to the global method tend to decay faster than those obtained due to the averaging method as the number of configurations increases. The experimental data obtained from just 10 target configurations result in estimation errors of about 0.35° and 1cm for the orientation and position parameters, respectively

Trailer angle estimation using radar point clouds

Algorithms for trailer control and backup need to keep track of the trailer angle, therefore, the angle needs to be determined. This work shows that the angle can be estimated using 2D point clouds collected from two automotive radars installed in the taillight fixtures of a trailer-coupled truck. The detection threshold of each radar is reduced to allow more detections of the trailer. A tracking procedure is introduced to find the trailer detections as the trailer rotates; the tracked set of detections are then compared with one or more reference sets to estimate the angle. Estimated angles are further refined with a Kalman filter to obtain smooth estimates of the angle. The algorithm is tested on 15 datasets; each dataset is obtained in an experiment in which the trailer is rotated up to about 40∘ in both radar directions. The estimates from all the datasets result in a global root mean squared error of about 0.9∘ for up to 10∘ absolute trailer rotation; about 1.3∘ for up to 20∘ absolute rotation; about 1.5∘ for up to 30∘ absolute rotation; and about 1.9∘ for all trailer angles considered. The algorithm executes in about 3 ms on a typical personal computer