Learn more about Advanced Driver Assistance Systems
Learn more about Advanced Driver Assistance Systems
Advanced Driver Assistance Systems
Rahul Kala, in On-Road Intelligent Vehicles, 2016
Advanced Driver Assistance Systems are intelligent systems that reside inside the vehicle and assist the main driver in a variety of ways. These systems may be used to provide vital information about traffic, closure and blockage of roads ahead, congestion levels, suggested routes to avoid congestion etc. These systems may also be used to judge the fatigue and distraction of the human driver and thus make precautionary alerts or to assess the driving performance and make suggestions regarding the same. These systems can take over the control from the human on assessing any threat, perform easy tasks (like cruise control) or difficult manoeuvres (like overtaking and parking). The greatest advantage of using the assistance systems is that they enable communication between different vehicles, vehicle infrastructure systems and transportation management centres. This enables exchange of information for better vision, localization, planning and decision making of the vehicles.
Embedded Software for Automotive Applications
Inga Harris, in Software Engineering for Embedded Systems, 2013
Advanced driver-assistance systems (ADAS) are one of the fastest-growing safety application areas due to the desire to reduce vehicular accidents and fatalities.
Beyond passive safety systems, active safety systems play a major role in reducing traffic fatalities and the monetary impact of vehicular accidents. ADAS systems include long- and medium-range radar and vision systems. Developing an ADAS system requires state-of-the-art yet cost-effective RF technology that can be embedded in the vehicle for exterior object detection and classification. A state-of-the-art radar system can tell a vehicle from a pedestrian, from a wall, and know the location and potential corrective path. Extraordinary computation power is needed to make the system efficient, but to become more prevalent in the marketplace the cost must be very low.
Active safety systems include adaptive cruise control (ACC) and collision-warning systems with automatic steering and braking intervention. In a collision-warning system, a microcontroller-controlled 77 GHz transmitter emits signals reflected from objects ahead, to the side and to the rear of the vehicle, which are captured by multiple receivers integrated throughout the vehicle. Using a high-performance 32-bit single- or dual-core microcontroller with embedded flash and RAM, the radar system can detect and track objects in the frequency domain, triggering a driver warning of an imminent collision and initiating ESC emergency intervention.
Camera systems in ADAS can display what is behind or beside the vehicle, even at night on screen. They can also analyze the video content for automatic lane-departure warning systems and high/low-beam headlight control. An image sensor interface provides incoming video frames to a single- or dual-core architecture optimized with DSP extensions for image improvement filtering and edge or spot detection. Additional system requirements include an appropriate communication interface, an integrated DRAM interface for fast access to external memory and embedded flash for low system cost.
Multi-Agent Active Collaboration Between Drivers and Assistance Systems
Jean-Paul A. Barthès, Philippe Bonnifait, in Advances in Artificial Transportation Systems and Simulation, 2015
Advanced Driver Assistance Systems (ADAS) are systems intended to help the driver in his driving activities. Technological solutions are many, like Adaptive Cruise Control (ACC), Intelligent Speed Adaptation (ISA) or Collision Warning Systems (CWS). When designed with a safe Human–Machine Interface (HMI), an ADAS should increase car safety and comfort.
Building a safe HMI requires careful attention as argued by ergonomics. For instance, Bruyas et al. (1998) have proposed guidelines to display information to the driver in running conditions. In this kind of problem, much effort is devoted to the choice of an efficient and safe strategy to display the information coming from the perception system of the vehicle or from cooperative perception, thanks to communication devices. Here, the driver is uniquely receiving information from the warning system. It is up to him to take into account the warning messages.
Some ADAS systems act in a completely different way, directly into the control inputs of the vehicle. Obstacle detection systems like the one presented by Broggi et al. (2002) can take the decision to brake, if the situation is estimated “very” dangerous. This kind of system is usually classified as “active safety”. For some ergonomists, ADAS of this kind are called “dead driver systems” (Hoc and Debernard, 2002).
For several years, people focused on systems operating between these two extremes. Such approaches are sometimes referenced as “cognitive” (Althoff et al., 2007; Heide and Henning, 2006). Here, the problem is to devise a closer collaboration between the driver and the machine. Monitoring the driver’s activities is the first key prerequisite (Murphy-Chutorian and Trivedi, 2010) but the problem goes far beyond. A collaboration can take place between the human and the machine to modify the setting of the parameters of the ADAS, for instance. A basic example is the problem of giving the destination address to the navigation system in order to compute a route. The collaboration can also occur during the operation, while driving. This is typically the issue we consider is this chapter, by proposing to use a multi-agent system (called OMAS in the following) that acts as an interface between the driver and the ADAS, a real-time platform, PACPUS, managing sensors, collecting data from the vehicle, and implementing the ADAS function. By using speech recognition, the system presented here is able to understand sentences relative to the tuning of warning messages due to speeding in dangerous situations.
The remaining chapter is organized in five sections. We start by studying the classical role of multi-agent architectures for intelligent control in robotics. We present afterward a use case in which an ADAS system collaborates actively with the driver at a cognitive level. Then we detail the system that has been developed for this purpose. We report a critical review of the results that we obtained and we conclude by presenting future extensions of this research.
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