Real-Time Vehicular Wireless System-Level Simulation

The performance of wireless communication systems is fundamentally determined by the properties of the underlying wireless communication channel. Vehicular communication channels exhibit time-variant multi-path propagation with non-stationary channel statistics. Thus, channel emulation tools for the reproducible test of wireless communication systems are urgently needed to enable the development of ultra-reliable low-latency communication links. In this paper we validate the vehicle-in-the-loop (ViL) test of vehicle-to-everything (V2X) communication links by means of time-variant channel emulation. The validation is performed by comparing the received signal strength indicator (RSSI) and packet error rate (PER) of measurements on a proving ground with the RSSI and PER obtained from ViL tests. For the ViL tests, the wireless communication channel is emulated using a geometry-based stochastic channel model, which is updated in real-time, dependent on the position and velocity of the vehicles. We collect results of different scenarios on the proving ground and from ViL tests. The results show an qualitative match between ViL test and measurement on the proving ground. An exact quantitative match can be obtained with the calibration parameters from the measurements.

Performance of a code-division multiple-access (CDMA) system is studied. The frame-error rate (FER) and bit-error rate (BER) of the forward traffic channel are obtained by using a simulation system based on TIA/EIA/IS-95. A diversity receiver structure with an adaptive search engine is developed and analyzed. The adaptive rate of the search engine is optimized to achieve high diversity gain. The diversity gain factor is derived based on a cluster of wide-band tapped delay-line multipath fading models. The relationships between receiver diversity gain, multipath channel characteristics, and the number of distributed antennas are derived. The multipath channel characteristics have a major impact on the system performance. Simulation shows that when channel mean delay spread is less than the chip interval, a distributed antenna system is essential for improving the system capacity. When more users are served in the system, FER is more sensitive to the channel characteristics. Uncaptured energy acts as self-interference, which can increase the BER by an order of magnitude and reduce system capacities, FER could also be improved by reducing the resolution of the diversity receiver branches to one half of the chip interval. The system performance for the indoor and outdoor environments are evaluated by utilizing the proposed diversity receiver over typical personal communications services channel models specified by the Joint Technical Committee (JTC).

Shallow-water horizontal underwater acoustic (UWA) channels are characterized by a time-varying multipath structure with a long delay spread. This causes significant signal distortion in phase coherent transmissions by introducing intersymbol interference (ISI), which can span several hundreds of symbols. The multipath structure is usually very sparse. This paper proposes an adaptive sparse partial response equalizer (SPRE) well matched with the channel characteristics to mitigate the ISI efficiently, and communicate reliably. Unlike the conventional equalizers that try to eliminate the ISI completely, the proposed structure performs equalization in two steps. In the first step, the channel is equalized to a judiciously chosen target impulse response (TIR), thereby leaving controlled residual ISI at the output of the SPRE. In the second step, the residual ISI is further mitigated by a near-optimal, graph-based, low-complexity symbol detection algorithm known as belief propagation (BP). We demonstrate that due to the sparse nature of the long ISI channel, and the well-matched adaptive TIR, this two-step equalization process is particularly useful. The idea here can be compared to what is done in the magnetic recording literature [i.e., partial response maximum-likelihood (ML) equalization] to avoid excessive noise enhancement, with the difference that the TIR needs to be designed adaptively using training symbols and that in the second step, instead of using a ML detector, a BP algorithm is used. The proposed SPRE algorithm is designed for a general multiple-input-multiple-output (MIMO) system and it provides soft outputs for the transmitted bits as a by-product of the BP-type equalization algorithm. Hence, it can be efficiently employed in coded systems (e.g., turbo or low-density-parity-check-coded, interleaved systems) with an iterative decoding/equalization scheme, providing significant improvements in achievable error rates. Finally, we note that the proposed SPRE scheme also incorporates a phase locked loop (PLL) structure so as to track the Doppler shift introduced by the channel. To evaluate the effectiveness of the proposed equalizer, we compare it with several decision feedback equalizers (DFEs) proposed by Stojanovic (J. Ocean. Eng. vol. 19, no. 1, pp. 100-111, Jan. 1994), Roy (Proc. MTS/IEEE TECHNO-OCEAN Conf., vol. 1, pp. 26-33, Nov. 2004), and Roy (J. Ocean. Eng., vol. 32, no. 3, pp. 663-688, Jul. 2004) both through simulations and experimental results. The results demonstrate that the SPRE receiver outperforms the decision-feedback-based receivers when the channel has long, yet sparse (possibly, nonminimum phase) structure, by reducing the frame error rates (FERs), improving the post-equalization signal-to-noise ratio (SNR), and decreasing the required number of turbo iterations (reduced latency).

Like today's autonomous vehicle prototypes, vehicles in the future will have rich sensors to map and identify objects in the environment. For example, many autonomous vehicle prototypes today come with line-of-sight depth perception sensors like 3D cameras. These cameras are used for improving vehicular safety in autonomous driving, but have fundamentally limited visibility due to occlusions, sensing range, and extreme weather and lighting conditions. To improve visibility and performance, not just for autonomous vehicles but for other Advanced Driving Assistance Systems (ADAS), we explore a capability called Augmented Vehicular Reality (AVR). AVR broadens the vehicle's visual horizon by enabling it to share visual information with other nearby vehicles, but requires careful techniques to align coordinate frames of reference, and to detect dynamic objects. Preliminary evaluations hint at the feasibility of AVR and also highlight research challenges in achieving AVR's potential to improve autonomous vehicles and ADAS.

Cameras are an essential component of many Advanced Driver Assistance Systems (ADAS) such as Lane Departure Warning, Lane Keeping Assistance, Automatic Emergency Braking, Traffic Sign Recognition and Night Vision. With the ongoing evolution of ADAS towards Automated Driving, the amount of cameras installed in a vehicle is expected to increase. At the same time, the performance of such cameras needs to match more stringent requirements. Nowadays automotive cameras are typically sensitive beyond the visual range (i.e. into the near infrared range). They may also use up to 20 bits precision for their internal signal. In order to support the development process of ADAS and Automated Driving Systems by means of simulation, it is required to have detailed physically correct simulation models of these automotive cameras. This paper describes the need for physics-based simulation and introduces a new spectral HDR camera model in the ADAS simulation platform PreScan. The traditional RGB-based camera implementation created images limited to the visual spectrum. In contrast, the new PreScan spectral camera model allows for high color depth, broad spectrum and physically correct image generation. The model provides a real-time simulation of light in the 320-1500nm spectral range which traverses through the various media and lenses before the sensor produces the final image signal. Other important features of this new spectral HDR camera model are a depth resolution of 20 bit per waveband as well as an increased dynamic range.

Knowledge of the tire-road friction condition is an important key for driver safety and vehicle stability systems during ice and snow road. In particular, friction information can be used to enhance the performance of Advanced Driver-Assistance Systems (ADAS) applications providing real-time forces condition. The paper deals with a method for real-time identification of tyre/road friction condition using both Pacejka model and steady state form of distributed LuGre to obtain the friction based on recursive nonlinear optimization of the curve fitting errors. Finally, the effectiveness of the method is confirmed by real-time simulations in ice and snow conditions.

In this paper a low-cost link level performance prediction technique is proposed for a single input and multiple output system. Receiver link level abstraction is used in system level simulations of large networks in order to reduce their complexity. Usually, a single lookup table is employed in link level abstraction to predict a receiver's performance under different channel conditions. In the presented work, the mean frame error rate curve of different diverse channels is proposed as the reference for performance prediction in single input multiple output systems. Its generation involves compression of the received code word into a single quality measure based on the postdetection signal to noise ratio values using nonlinear capacity, exponential, and mutual mapping functions. The overall performance difference between simulated and predicted frame error rates shows that the proposed scheme gives very good performance approximations under different modulation and coding schemes, clearly outperforming the classical line of sight channel lookup table.

Traffic in the near future is expected to be a mix of legacy vehicles with limited number of on-board sensors and sensor-rich vehicles with advanced sensing capabilities and different levels of automation. In this work, we propose a novel framework to leverage the existence of sensor-rich vehicles to assist legacy vehicles in estimating the road geometry which is an essential task for advanced driver assistance systems (ADAS). In the proposed method, the legacy vehicle, which is not necessarily equipped with any cameras or ranging sensors, receives processed camera data related to the road geometry from nearby sensor-rich vehicles. Then, the legacy vehicle fuses this data to build a local map of the road ahead for up to 200 m. Using experimental data, we show that the proposed method reduces the root mean square estimation error by 209% and the mean absolute estimation error by 857% compared to camera-based systems. The results also show that sensor-rich vehicles benefit from sharing the processed camera data and can significantly improve the accuracy of the road geometry estimate at much higher distances.

A real-time multisensor fusion verification framework for advanced driver assistance systems

Due to rapid changes in the environment, vehicular communication channels no longer satisfy the assumption of wide-sense stationary uncorrelated scattering. The non-stationary fading process can be characterized by assuming local stationarity regions with finite extent in time and frequency. The local scattering function (LSF) and channel correlation function (CCF) provide a framework to characterize the mean power and correlation of the non-stationary channel scatterers, respectively. In this paper, we estimate the LSF and CCF from measurements collected in a vehicle-to-infrastructure radio channel sounding campaign in a suburban environment in Lille, France. Based on the CCF, the stationarity region is evaluated in time as 567 ms and used to capture the non-stationary fading parameters. We obtain the time-varying delay and Doppler power profiles from the LSF, and we analyze the corresponding root-mean-square delay and Doppler spreads. We show that the distribution of these parameters follows a lognormal model. Finally, application relevance in terms of channel capacity and diversity techniques is discussed. Results show that the assumption of ergodic capacity and the performance of various diversity techniques depend on the stationarity and coherence parameters of the channel. The evaluation and statistical modeling of such parameters can provide a way of tracking channel variation, hence, increasing the performance of adaptive schemes.

Internet of Vehicles (IoV) enables advanced driver assistance systems (ADAS) to acquire the sensing information of nearby vehicles for driving safety. One of the most dangerous driving maneuvers is overtaking, where the leading vehicle may occlude the view of the following vehicle's driver. To assist the driver, providing the capability of see-through car is a promising solution. ADAS with IoV can obtain the videos from the leading vehicle to compensate the occluded view. Therefore, we formulate an image synthesis problem and propose a lightweight solution. Compared to other works, we expect that our system can easily apply on a mobile device with a camera or a smartphone. For implementation, we utilize Unity with Vuforia to make pose estimation, rather than GPS or 3D reconstruction. We also validate the performance of our system by the prototyping result. It shows that the stitching performance is well and we can achieve this AR system with low costs at price and computation.

The realistic channel environment can be characterized by a geometry-based stochastic channel (GBSC) model composed of multiple clusters, each of which consists of many rays. For the GBSC model, the power angle of arrival (AoA) spectrum of a cluster depends on the power angle of departure-Doppler (AoD-Doppler) spectrum. Hence, this model is non-Kronecker structured. In over-the-air (OTA) testing, the prefaded signal synthesis (PFS) method is valid for emulating the GBSC to measure devices under test (DUTs). However, the emulated channel under the conventional PFS method is a Kronecker case due to the independently and identically distributed (i.i.d.) fading sequences between probes when synthesizing clusters. It will lead to inaccurate link characteristics for emulation. Therefore, a non-Kronecker structured PFS (NKSPFS) method is proposed. Specifically, the rays mapped to probes are individually weighted to ensure that fading sequences between probes are independent but not identically distributed. To determine the ray weights without degrading marginal correlations for emulation, the constraints are introduced on the deviation between the target spatial–temporal correlation and the emulated spatial–temporal correlation according to the emulated spatial correlation and temporal correlation. Simulation results show that the proposed NKSPFS method outperforms the conventional PFS method in OTA testing of multi-antenna devices.

Recent advances in computer and communications technologies have made possible the interconnection of large number of real-time training simulators via local area networks. The self-healing nature of real-time networked simulation has been found to allow for a modification based on discarding old packets whenever new state updates become available. The performance benefits obtained by implementing this modification at both the application and data-link layers are presented for two medium-access network protocols: ETHERNET and GBRAM. An analysis of a phenomenon, called the greedy node problem, in distributed simulation networks is presented.

With the emergence of autonomous vehicles and internet of vehicles (IoV), future roads of smart cities will have a combination of autonomous and automated vehicles with regular vehicles that require human operators. To ensure the safety of the road commuters in such a network, it is imperative to enhance the performance of Advanced Driver Assistance Systems (ADAS). Real-time driving risk prediction is a fundamental part of an ADAS. Many driving risk prediction systems have been proposed. However, most of them are based only on vehicle’s velocity. But in most of the accident scenarios, other factors are also involved, such as weather conditions or driver fatigue. In this paper, we proposed an accident prediction system for Vehicular ad hoc networks (VANETs) in urban environments, in which we considered the crash risk as a latent variable that can be observed using multi-observation such as velocity, weather condition, risk location, nearby vehicles density and driver fatigue. A Hidden Markov Model (HMM) was used to model the correlation between these observations and the latent variable. Simulation results showed that the proposed system has a better performance in terms of sensitivity and precision compared to state of the art single factor schemes.

Abstract: This project introduces a low-cost prototype of an Advanced Driver Assistance System (ADAS) that integrates Doppler radar with computer vision to enhance vehicle safety. The system is designed to detect obstacles in real time, estimate the speed of nearby vehicles, and recognize road lanes, thereby assisting drivers in navigating complex traffic situations. The Doppler radar sensor provides reliable velocity measurements even in poor visibility conditions such as fog or rain, while the vision module, based on lane detection algorithms, helps in maintaining proper road alignment. Together, these components form a sensor fusion framework that reduces accident risks by delivering accurate and timely driving assistance. The hardware implementation is based on affordable and widely available components, including the HB100 Doppler radar module, a standard camera, and a Raspberry Pi 4. The Raspberry Pi acts as the central processing unit, executing both radar signal processing and computer vision tasks. By eliminating the need for expensive automotive-grade systems, this design makes ADAS functionalities more accessible for low-cost applications, particularly in regions where advanced safety systems are not yet common. Testing in a simulated environment has shown promising results, demonstrating that the system performs effectively across varied weather and lighting conditions. Radar ensures robust speed detection regardless of external factors, while the camera enables contextual awareness of road structures. This synergy enhances reliability and sets the stage for future improvements. Further advancements may include machine learning-based object classification, integration of additional sensors like LiDAR, and optimization for real-world deployment. The outcomes suggest that combining Doppler radar and embedded vision can provide a practical pathway toward safer and smarter driving technologies..