Blind Spot Detection System Research Articles

A blind spot detection and warning system based on millimeter wave radar for driver assistance

This paper proposed a blind spot detection and warning system named BSDW for driver assistance under daytime and nighttime conditions. The proposed BSDW system includes system architecture platform, radar hardware system structure, intermediate frequency signal processing, radar target detection algorithms, blind spot area calibration method, system control strategy and system integration. Line frequency modulated continuous wave millimeter-wave radar was used to detect moving targets which come into the rear blind spot alarm area, including left, right and behind area of the subject vehicle. Based on Rayleigh clutter distribution model, a cell greatest, smallest and averaging constant false-alarm rate target detection algorithm named CGSA-CFAR was proposed to maintain higher detection rate and lower false-alarm rate by adjusting power detection threshold in time based on the noise power level, which was estimated according to the proposed target detection algorithm. The proposed system was implemented on embedded hardware platform and verified on the Chery Arrizo7 vehicle. Under urban daytime and nighttime conditions, the early warning rates of the proposed system were up to 98.20% and 98.21% respectively. The results show that the proposed system can detect the targets which come into the rear warning area of the subject vehicle and give early alarm to driver effectively in various urban road environments.

In-depth understanding of lane changing interactions for in-vehicle driving assistance systems

Lane-changing events are often related with safety concern and traffic operational efficiency due to complex interactions with neighboring vehicles. In particular, lane changes in stop-and-go traffic conditions are of keen interest because these events lead to higher risk of crash occurrence caused by more frequent and abrupt vehicle acceleration and deceleration. From these perspectives, in-depth understanding of lane changes would be of keen interest in developing in-vehicle driving assistance systems. The purpose of this study is to analyze vehicle interactions using vehicle trajectories and to identify factors affecting lane changes with stop-and-go traffic conditions. This study used vehicle trajectory data obtained from a segment of the US-101 freeway in Southern California, as a part of the Next Generation Simulation (NGSIM) project. Vehicle trajectories were divided into two groups; with stop-and-go and without stop-and-go traffic conditions. Binary logistic regression (BLR), a well-known technique for dealing with the binary choice condition, was adopted to establish lane-changing decision models. Regarding lane changes without stop-and-go traffic conditions, it was identified based on the odd ratio investigation that he subject vehicle driver is more likely to pay attention to the movement of vehicles ahead, regardless of vehicle positions such as current and target lanes. On the other hand, the subject vehicle driver in stop-and-go traffic conditions is more likely to be affected by vehicles traveling on the target lane when deciding lane changes. The two BLR models are adequate for lane-changing decisions in normal and stop-and-go traffic conditions with about 80 % accuracy. A possible reason for this finding is that the subject vehicle driver has a tendency to pay greater attention to avoiding sideswipe or rear-end collision with vehicles on the target lane. These findings are expected to be used for better understanding of driver’s lane changing behavior associated with congested stop-and-go traffic conditions, and give valuable insights in developing algorithms to process sensor data in designing safer lateral maneuvering assistance systems, which include, for example, blind spot detection systems (BSDS) and lane keeping assistance systems (LKAS).

Valuation of active blind spot detection systems by younger and older adults

Due to their disproportional representation in fatal crashes, younger and older drivers both stand to benefit from in-vehicle safety technologies, yet little is known about how they value such technologies, or their willingness to adopt them. The current study investigated older (aged 65 and greater; N=49) and younger (ages 18–23; N=40) adults’ valuation of a blind spot monitor and asked if self-reported visual difficulties while driving predicted the amount participants were willing to pay for a particular system (BMW’s Active Blind Spot Detection System) that was demonstrated using a short video. Large and small anchor values ($250 and $500, respectively) were used as between subjects manipulations to examine the effects of initial valuation, and participants proceeded through a short staircase procedure that offered them either the free installation of the system on their current vehicle or a monetary prize ($25–$950) that changed in value according to which option they had selected in the previous step of the staircase procedure. Willingness to use other advanced driver assistance systems (lane-departure warning, automatic lane centering, emergency braking, adaptive cruise control, and self-parking systems) was also analyzed, additionally controlling for prior familiarity of those systems. Results showed that increased age was associated with a higher valuation for the Active Blind Spot Detection System in both the large and small anchor value conditions controlling for income, gender, and technology self-efficacy. Older adults valued blind spot detection about twice as much ($762) as younger adults ($383) in the large anchor condition, though both groups’ values were in the range for the current cost of an aftermarket system. Similarly, age was the most robust positive predictor of willingness to adopt other driving technologies, along with system familiarity. Difficulties with driving-related visual factors also positively predicting acceptance levels for adaptive cruise control and emergency braking systems. Results are discussed in comparison to older adults’ willingness to pay for other home-based assistive technologies aimed at improving well-being and independence.

Potential Safety Benefit of the Blind Spot Detection System for Large Trucks on the Vulnerable Road Users in Taiwan

Considering motorcyclists, pedestrians and bicyclists as vulnerable road users (VRUs), more than 75 percent of the victims of fatal crashes involving large trucks in Taiwan are VRUs. Most crashes occurred at or were due to the blind spots of large trucks because of the size and traveling locations of the VRUs. This study applies typology and statistical methods to estimate the potential safety benefit of blind spot detection (BSD) systems for large trucks on VRUs. The pre-crash scenarios associated with the blind spots of large trucks were derived by counting the maneuvers of large trucks and VRUs, prior to crashes, the truck drivers’ improper behaviors (cause of crashes), and the crash types. The number of crashes and fatalities were counted for the pre-crash scenario relevant to the BSD systems. A value of 0.8 of human machine interface factor (HMIF) based on a previous study was applied to estimate the potential safety benefits of the BSD system. The results show that the implementation of BSD systems on all large trucks could help avoid about 24, 10, and 11 percent of large truck-involved crashes with pedestrians, bicycles, and motorcycles, respectively. The BSD systems could also save 5 pedestrians, 3 bicyclists, and 15 motorcyclists per year from crashes involving large trucks.

Robust estimation of vehicle recognition on curved roads using a rear-side view vision system

This paper presents a method to estimate the relative distance and speed of vehicles approaching from the side and rear for a blind spot detection system or lane change assistance. Vehicles with the same relative distance can be assigned different pixel positions on the image frame depending on the road curvature. Thus, based on a lane model and vehicle dynamics, a Perception-Net estimation algorithm has been designed to recognize the rear-side lanes and their curvature under various noisy environments. Vehicles approaching from the rear are detected by calculating the velocity vector of feature flows, which are a type of motion analysis, on the image frame. Through various simulations, it is concluded that the proposed method can provide robust estimations of the relative distance and speed between vehicles with satisfactory error tolerance.

Multifeature Fusion Vehicle Detection Algorithm Based on Choquet Integral

Vision-based multivehicle detection plays an important role in Forward Collision Warning Systems (FCWS) and Blind Spot Detection Systems (BSDS). The performance of these systems depends on the real-time capability, accuracy, and robustness of vehicle detection methods. To improve the accuracy of vehicle detection algorithm, we propose a multifeature fusion vehicle detection algorithm based on Choquet integral. This algorithm divides the vehicle detection problem into two phases: feature similarity measure and multifeature fusion. In the feature similarity measure phase, we first propose a taillight-based vehicle detection method, and then vehicle taillight feature similarity measure is defined. Second, combining with the definition of Choquet integral, the vehicle symmetry similarity measure and the HOG + AdaBoost feature similarity measure are defined. Finally, these three features are fused together by Choquet integral. Being evaluated on public test collections and our own test images, the experimental results show that our method has achieved effective and robust multivehicle detection in complicated environments. Our method can not only improve the detection rate but also reduce the false alarm rate, which meets the engineering requirements of Advanced Driving Assistance Systems (ADAS).

Millimeter Wave Radar for Automotive Blind Spot Detection System

Considering the problem of rearview mirror blind spot during driving, the paper studied and designed the blind spot detection system based on MMW radar. Radar was installed at an appropriate position on the detection target signal by transmitting, when another car enter the detecting area, the small alarm light beside A pillar would shine or alarm few times, to remind drivers careful change road. And the effect would not effect by weather or time. For the radar sensor application environment, triangle wave LFMCW can effectively solve the speed from the coupling phenomenon. The paper showed experimental and simulation data.

A method to investigate drivers’ acceptance of Blind Spot Detection System®

Lately, with the goal of improving road safety, car makers developed and commercialised some Advanced Driver Assistance Systems (ADAS) which, through the detection of blind spot areas on the vehicle's sides, could help the drivers during the overtaking and the change lane task. Despite the possible benefits to reduce lateral crashes, the overall impact on road safety of such systems have not been deeply studied yet; notably, despite some researches have been carried out, there is a lack of studies regarding the long-term usage and drivers' acceptance of those systems. In order to fill the research gap, a methodology, based on the combination of focus groups interviews, questionnaires and a small-scale field operational test (FOT), has been designed in this study; such a methodology aims at evaluating drivers' acceptance of Blind Spot Information System® and at proposing some ideas to improve the usability and user-friendliness of this (or similar) device in their future development.

Isrss: Integrated side/rear safety system

As driver assistant systems (DAS) and active safety vehicles (ASV) with various functions become popular, it is not uncommon for multiple systems to be installed on a vehicle. If each function uses its own sensors and processing unit, it will make installation difficult and raise the cost of the vehicle. As a countermeasure, research integrating multiple functions into a single system has been pursued and is expected to make installation easier, decrease power consumption, and reduce vehicle pricing. This paper proposes a novel side/rear safety system using only one scanning laser radar, which is installed in the rear corner of the driver’s side. Our proposed system, ISRSS (integrated side/rear safety system), integrates and implements four system functions: BSD (blind spot detection), RCWS (rear collision warning system), semi-automatic perpendicular parking, and semi-automatic parallel parking. BSD and RCWS, which operate while the vehicle is running, share a common signal processing result. The target position designation for perpendicular parking and parallel parking situations is based on the same signal processing. Furthermore, as system functions during running and those during automatic parking operate in exclusive situations, they can share common sensors and processing units efficiently. BSD and RCWS system functions were proved with 13025 and 2319 frames, respectively. The target position designation for perpendicular and parallel parking situations was evaluated with 112 and 52 situations and shows a success rate of 98.2% and 92.3%, respectively.