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Abstract In Advanced Driver Assistant System (ADAS), the automotive radar is used to detect targets or obstacles around the vehicle. The procedure of Constant False Alarm Rate (CFAR) plays an important role in adaptive targets detection in noise or clutter environment. But in practical applications, the noise or clutter power is absolutely unknown and varies over the change of range, time and angle. The well-known cell averaging (CA) CFAR detector has a good detection performance in homogeneous environment but suffers from masking effect in multi-target environment. The ordered statistic (OS) CFAR is more robust in multi-target environment but needs a high computation power. Therefore, in this paper, a new two-dimension CFAR procedure based on a combination of Generalized Order Statistic (GOS) and CA CFAR named GOS-CA CFAR is proposed. Besides, the Linear Frequency Modulation Continuous Wave (LFMCW) radar simulation system is built to produce a series of rapid chirp signals.

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The objective of this study was to investigate if 3D auditory displays could be used to enhance parking assistance systems (PAS). Objective measurements and estimations of workload were used to assess the benefits of different 3D auditory displays. In today’s cars, PAS normally use a visual display together with simple sound signals to inform drivers of obstacles in close proximity. These systems rely heavily on the visual display, as the sound does not provide information about obstacles' location. This may cause the driver to lose focus on the surroundings and reduce situational awareness. Two user studies (during summer and winter) were conducted to compare three different systems. The baseline system corresponded to a system normally found in today’s cars. The other systems were designed with a 3D auditory display, conveying information of where obstacles were located through sound. A visual display was also available. Both normal parking and parallel parking was conducted.

The radar-based advanced driver assistance systems (ADAS) like autonomous emergency braking (AEB) and forward collision warning (FCW) can reduce accidents, so as to make vehicles, drivers and pedestrians safer. For active safety, automotive millimeter-wave radar is an indispensable role in the automotive environmental sensing system since it can work effectively regardless of the bad weather while the camera fails. One crucial task of the automotive radar is to detect and distinguish some objects close to each other precisely with the increasingly complex of the road condition. Nowadays almost all the automotive radar products work in bidimensional area where just the range and azimuth can be measured. However, sometimes in their field of view it is not easy for them to differentiate some objects, like the car, the manhole covers and the guide board, when they align with each other in vertical direction.

Multi-Target tracking is a central aspect of modeling the surrounding environment of autonomous vehicles. Automotive millimeter-wave radar is a necessary component in the autonomous driving system. One of the biggest advantages of radar is it measures the velocity directly. Another big advantage is that the radar is less influenced by environmental conditions. It can work day and night, in rainy or snowy conditions. In the expressway scenario, the forward-looking radar can generate multiple objects, to properly track the leading vehicle or neighbor-lane vehicle, a multi-target tracking algorithm is required. How to associate the track and the measurement or data association is an important question in a multi-target tracking system. This paper applies the nearest-neighbor method to solve the data association problem and uses an extended Kalman filter to update the state of the track.

The On-Board Diagnostics II (OBD-II) port began as a means of extracting diagnostic information and supporting the right to repair. Self-driving vehicles and cellular dongles plugged into the OBD-II port were not anticipated. Researchers have shown that the cellular modem on an OBD-II dongle may be hacked, allowing the attacker to tamper with the vehicle brakes. ADAS, self-driving features and other vehicle functions may be vulnerable as well. The industry must balance the interests of multiple stakeholders including Original Equipment Manufacturers (OEMs) who are required to provide OBD function, repair shops which have a legitimate need to access the OBD functions, dongle providers and drivers. OEMs need the ability to protect drivers and manage liability by limiting how a device or software application may modify the operation of a vehicle.

Abstract Collision alert and avoidance systems (CAS) could help to minimize driver errors. They are instrumental as an advanced driver-assistance system (ADAS) when the vehicle is facing potential hazards. Developing effective ADAS/CAS, which provides alerts to the driver, requires a fundamental understanding of human sensory perception and response capabilities. This research explores the premise that external stimulation can effectively improve drivers’ reaction and response capabilities. Therefore this article proposes a light-emitting diode (LED)-based driver warning system to prevent potential collisions while evaluating novel signal processing algorithms to explore the correlation between driver brain signals and external visual stimulation. When the vehicle approaches emerging obstacles or potential hazards, an LED light box flashes to warn the driver through visual stimulation to avoid the collision through braking.

Car Periphery Supervision (CPS) systems comprise a family of automotive systems that are based on sensors installed around the vehicle to monitor its environment. The measurement and evaluation of sensor data enables the realization of several kinds of higher level applications such as parking assistance or blind spot detection. Although a lot of similarity can be identified among CPS applications, these systems are traditionally built separately. Usually, each single system is built with its own electronic control unit, and it is likely that the application software is bound to the controller's hardware. Current systems engineering therefore often leads to a large number of inflexible, dedicated systems in the automobile that together consume a large amount of power, weight, and installation space and produce high manufacturing and maintenance costs.