You can get all of the details from the conference paper or the MATLAB code, but I thought you might enjoy this video that explains some of the highlights of the paper and includes some animations of the different robot designs compared in the paper:]]>
Vehicular accidents are costly. Not only do they end lives, injure travelers, and destroy assets, but they also inspire excessively large, heavy, and inefficient vehicles. Active safety systems can assist error-prone human drivers in avoiding accidents and thereby improve safety, efficiency, and cost. Active safety systems existing today are fundamentally limited in their inability to accurately quantify threat and intervene in more than one dimension to assist the human driver in avoiding it. As such, these systems must be implemented in an ad-hoc fashion, requiring significant fine-tuning to avoid conflicts in their sometimes-competing objectives.
What we have created is an integrated (read: ‘all-in-one’) planning and control framework that performs all of the functions of existing safety systems, in addition to predictively avoiding future hazards. This framework uses a fundamentally-new and incredibly-useful threat assessment method to predict the danger or ‘threat’ posed to the vehicle given its current state and the state of its surroundings. Based on this threat assessment, it then determines when, how, and to what degree it must intervene to ensure that the vehicle does not crash, lose control, or otherwise endanger its occupants. The controller is designed to allow the human driver as much control as possible in low threat scenarios and intervene only as necessary to keep the vehicle safe in high-threat scenarios. In the figures and videos that follow, I’d like to demonstrate a subset of the framework’s capabilities using figures and videos selected from the thousands of simulations and over 800 experimental trials that weíve used to vet it. Note that due to proprietary controls at Ford’s proving grounds, we were unable to record video of our Jaguar S-Type performing these maneuvers. Instead, we recorded telemetry data from each experiment and re-produced the results in high-fidelity simulation software (ADAMS/car).
Each of the videos below overlays the results from two simulations: the gray vehicle is controlled solely by a human driver model whereas the blue vehicle is also fitted with the semi-autonomous controller. In experimental trials, 8 different human drivers, each with different driving styles, were tested.
The experiments shown in the figure below illustrate the semi-autonomous controller’s ability to adjust its behavior to the preference and/or performance of the human driver. The upper plot shows the vehicle path as the driver drifted laterally in the lane (edges shown in gray). The lower subplot shows the proportion of available steering control assumed by the controller.
Note that by simply changing the threshold threat at which the controller intervenes, we can allow the human driver more or less control in low-threat scenarios (between X = 0 and 100 meters) without adversely affecting the controller’s ability to keep the vehicle safely within the lane in high-threat situations. Thus, an inexperienced or cautious driver might prefer more controller intervention all the time in order to smooth out mistakes, while a seasoned or more adventurous driver would prefer that the controller not intervene until this intervention was absolutely necessary. In the figure above, the red solid line represents an intervention function tuned to the more cautious driver while the magenta dash-dotted line shows the results of tuning the controller to more experienced driver. Notice that in both cases, the controller allowed the human to wander freely within the lane, while intervening as necessary to prevent unsafe lane departure. The black dashed line shows what happens when the controller is turned off.
The video below demonstrates the navigation framework’s performance in the presence of stationary hazards such as road edges, roadway obstacles (not shown), etc. In this simulation, the driver of both vehicles actively seeks to remain on the road surface — a difficult feat at 20 m/s (~44 mph).
Notice that including the semi-autonomous controller in the control loop not only keeps the vehicle stable, but also moderates the driver’s inputs in the process. Whereas the unassisted driver oversteers and loses control of the vehicle, the assisted driver notices that the vehicle is responding as desired and is thus more moderate in his steer commands. This allows him to maintain control of the vehicle. Moreover, allocating less than 50% of the available control authority to the controller (see green bar on the right) is sufficient to keep the vehicle on the navigable roadway and within 0.4 meters of the (invisible) line on the center of the roadway that the driver model is trying to track. The combined effect of both inputs (driver and controller) is a vehicle trajectory that more closely tracks the path the driver is trying to follow than the driver could accomplish on his own.
In scenarios where a drowsy, inattentive, or otherwise-impaired driver fails to steer around an impending threat, the semi-autonomous controller foresees the threat, gauges the control action necessary to avoid it, and if the driver does not respond appropriately, takes the necessary control to keep the vehicle safe. Once the threat has been reduced, it returns control to the driver. The video below demonstrates one such case.
In order to avoid moving hazards, the semi-autonomous framework predicts their future position and pre-emptively assists the driver in avoiding those regions of the environment. In both of the videos below, the human driver acts as though he doesnít see the vehicles up ahead (no steering input). In the first video, the controller recognizes that a passing opportunity is available and takes only as much control as necessary to execute that maneuver. The second video illustrates a slightly different case in which the yellow vehicle accelerates once the blue vehicle initiates a passing maneuver (weíve all known one). In this case, the controller behaves much like an alert driver would ñ seeking first to pass, then pulling back in behind the yellow vehicle as it accelerates.
I hope that the ideas discussed in this mini-series have provided a glimpse into the unique challenges and opportunities facing the emerging science of semi-autonomous control. While the issues and potential solutions weíve discussed in these four articles might seem a bit long-winded for a blog, they only scratch the surface of the technology, user studies, and legal infrastructure requirements that must be satisfied before these systems can be commercially implemented. Not the least of these considerations are driver acceptance issues. Almost everywhere I go to present this technology, one of the first questions I am asked is whether our system will come with an ‘OFF’ switch. Many people distrust the invisible face of automation and prefer to feel like they are in complete control. While we cannot completely concede the latter without sacrificing safety, we can certainly improve drivers’ perception and acceptance of autonomy by creating reliable, non-intrusive systems that modify driver inputs as little as possible while avoiding hazards. Significant work remains to be conducted in both human factors and usability studies before this research is road ready (my standard legal disclaimer), but I believe that at some time in the near future, it will be. Here’s to smaller, lighter, safer, and more efficient automobiles!
I’d like to thank Dr. James Allison for his invitation to contribute these articles. Writing them has been an exercise in making my research more understandable to non-technical readers. For those of you who would like more details (and believe me there are many), I would invite you to read any of the applicable papers/theses listed on my website. If you have further questions, or would like to continue the conversation offline, I would be more than happy to visit with you. Please feel free to send me an email and/or leave comments below.]]>
Significant Challenges Facing the Current State-of-the-Art
The first challenge facing existing systems is their inability to accurately capture the threat inherent to a particular scenario in a meaningful way. Because assimilating multiple sources of threat into a single, actionable metric is difficult, the majority of ADAS systems in existence today consider only one hazard at a time and limit their assistance to one dimension or another. For example, adaptive cruise controllers focus on the closest obstacle within the ego vehicle’s line of sight (longitudinal dimension) and apply the brakes as necessary to avoid rear-end collisions. Lane keeping controllers focus instead on the lateral dimension, neglecting longitudinal vehicle dynamics and collision hazards in order to maintain a desired position and/or heading within the lane. Like the child playing checkers for the first time, the tactical, near-term focus of these systems often fails to consider the effect of current evasive actions on future threat scenarios. For example, Figure 1 illustrates the predictions of an emergency braking system (solid green), along with those of a lateral collision avoidance controller (dashed blue). Without considering Obstacle B or the maneuver required to straighten out after it has passed Obstacle C, the obstacle avoidance system might consider a pass to the left of Obstacle C preferable to an emergency braking maneuver. Such a decision could be disastrous if either 1) Vehicle A cannot straighten out in time to avoid running off the road or 2) Vehicle A cannot avoid an oncoming Vehicle B once it has passed Vehicle C.
The second major problem facing existing driver assistance systems is closely related to the first: the myopic focus of existing systems on a single source of threat requires that a vehicle be equipped with multiple safety systems. For example, comprehensive assistance might require that the same vehicle be equipped with warning devices to alert the driver, anti-lock brakes to prevent skidding, yaw stability control to prevent loss of control, adaptive cruise control to prevent rear-ending the vehicle ahead, lane-keeping control to prevent wandering out of one’s lane, etc. While each of these systems may perform adequately by itself, their combined output when placed together on the same vehicle can lead to vehicle performance that is suboptimal at best and unpredictable or inconsistent at worst.
For instance, imagine a vehicle traveling quickly along a curve in road (illustrated in Figure 2). Wanting to avoid the large truck that is passing you on the inside of the turn, you drift toward the outer edge of your lane. As the lane departure warning begins to sound (adding to your already-heightened apprehension), the lane assist system takes control of the steering wheel, turning it hard left. As you swerve left, the car begins to roll right, whereupon the rollover alarm lights begin to flash and the roll stability controller engages, turning you back toward the outer edge of the road and again engaging the lane keeping controller. You can imagine how this scenario might continue. The point is that when distinct systems with disparate goals try to control the same vehicle, conflicts in their warnings and their steering, acceleration, and braking commands are bound to result. Figure 3 shows a (simplistic) schematic of how multiple systems arranged on the same vehicle can sometimes interact.
The Alternative: An Integrated, Semi-Autonomous Active Safety Framework
To summarize the above, existing active safety systems are both incomplete in their assessment of threat and ineffective at sharing control with the human driver. This is where we come in.
When I began working with Karl Iagnemma and Steve Peters at MIT’s Robotic Mobility Group a couple of years ago, we began exploring a promising new idea for semi-autonomous control. We based our system on the simple observation that most human drivers tend to operate within a field of safe travel as opposed to along a predetermined path. Thus, instead of starting with a simple avoidance path, we chose to define a corridor through the environment that avoids obstacles, road edges, and other hazards. Then rather than selectively replace the driver when s/he strayed from the automation-desired path, we chose to gradually blend the driver’s inputs with the controller’s – giving the driver free reign while s/he remained within the safe corridor and only intervening as the likelihood of his or her leaving that safe corridor or losing control, increased.
The framework we developed (illustrated in Figure 4) storyboards as follows:
As a human driver navigates the vehicle, forward-looking sensors detect road edges, identify and terrain features (slopes, holes, etc.), and localize obstacles. Based on this information, a safe corridor that avoids these hazards is defined. A mathematical model of the vehicle is then forward-simulated to determine the safest or most stable path through the corridor given the vehicle’s current state (position, velocity, roll angle, etc.) and the current state of the environment. Because this optimal trajectory can be considered the best maneuver that can possibly be performed given the current circumstance, it is then used to assess the (best case) threat posed to the vehicle. In other words, no matter how skilled the human driver is, s/he will be unable to perform any better than the “best case” avoidance maneuver. As this “best case” maneuver becomes more dangerous, so does any maneuver that the human might attempt. In low-threat scenarios, the human retains full control of the vehicle (K=0 for you sharp-eyed readers). As threat increases owing, for example, to the driver’s failing to make adequate preparations to avoid an obstacle, so does the level of control authority given to the autonomous controller (gain K in the Figure 4 below). In extreme scenarios, when the avoidance maneuver required to keep the vehicle safe becomes so severe that only an optimal maneuver can be expected to pull it off, gain K becomes one and the system effectively acts as an autonomous controller until threat is reduced to human-manageable levels.
Figure 5 illustrates what the predicted avoidance maneuver might look when the vehicle is at position 1 (low threat, full human control), as well as how that prediction might appear when the vehicle reaches position 2 (high threat, nearly-autonomous operation).
The video below illustrates the semi-autonomous controller in action. Corridor constraints are shown in black and green, with the vehicle’s trail in blue and the predicted trajectory in red. In this simulation, the human driver fails to see or respond to a hazard. As the system’s prediction begins to predict a more and more severe avoidance maneuver, it gradually asserts only enough control necessary to avoid the hazard before giving control back to the human driver.
To conclude, the system we’ve developed combines the environment’s many hazards into a single, safe corridor and moderates the driver’s steering and braking inputs as necessary to keep the vehicle within that corridor. In low threat scenarios, the driver maintains full control of the vehicle. As threat increases, the controller shares control with the human driver to ensure that the vehicle does not leave the safe corridor. By inherently considering multiple sources of threat in a single, unified framework, this approach provides a significant advantage over existing driver assistance systems.
In my next post, I’ll show how this framework performed in the over 800 experimental trials with 8 different human drivers on a Jaguar S-Type. Stay tuned.]]>
Abstracts are due by February 11th, and draft papers are due by February 18th. Click here to begin the submission process, and select DAC-7, which is part of the 37th Design Automation Conference (DAC). Articles will be reviewed before acceptance, and authors of accepted papers will have an opportunity to revise their submission after receiving feedback. If you have any questions or suggestions regarding the session or conference, please feel free to contact me, or post your ideas to the comments section below. Read below for more details.
Design engineers have the opportunity to improve quality of life and sustainability simultaneously through better design. One of the most significant areas engineering design has an an impact on is energy use. In addition to reducing consumption, we need to develop and put into service products and systems that use energy more efficiently. By using advanced design techniques, such as design optimization, incorporating more efficient technology, or simplifying systems and processes, engineers can help propel us toward energy sustainability.
Here is a description of the session from the conference website:
Design for Energy Efficiency: DAC-7
The ASME Design Automation Committee invites papers focused on design theory, innovation, or methods that enhance energy efficiency of energy consuming products or systems. Analytical design techniques that reduce energy consumption while maintaining or improving performance are of particular interest. Sample topics of interest include but are not limited to the following:
- Using optimization to improve energy efficiency
- Reducing energy consumption through process analysis and redesign
- Energy recovery and reuse
- Advanced/intelligent/alternative transportation systems
- Novel control techniques that reduce energy consumption
- Efficient energy storage
- Challenges in transitioning to more efficient technologies
- Economics of energy efficient technology
- Energy savings through system simplification
Vehicular safety is a problem that, I think, needs little motivation. Recent traffic safety reports from the National Highway Traffic and Safety Administration show that in 2008 alone, over 37,000 people were killed and another 2.3 million injured in motor vehicle accidents in the United States. While the longstanding presence of collision mitigation systems (seat belts, roll cages, crumple zones, etc.) has contributed to a decline in these numbers from previous years, it has failed to eliminate collisions altogether and has limited engineers’ ability to create smaller, lighter, and more energy-efficient vehicles. This is where Advanced Driver Assistance Systems (ADAS) – systems designed to avoid hazards altogether – come in.
Advanced Driver Assistance Systems (ADAS) in use today can be (roughly) placed into four classes. The first of these classes can be said to be perhaps the most hands-off of the driver assistance techniques. Systems that fall into this class are broadly known as driver-warning systems and include lane departure warning systems, lane change assistance systems, and collision warning systems (sometimes imprecisely referred to as “collision avoidance systems”), among others. Driver warning systems typically provide feedback to the driver on visual, audible, or in some cases haptic (touch) channels. Research in this area is both active and complex owing largely to the immense variability that exists between human drivers and the consequent difficulty of predicting exactly how each will respond to various warning cues. For example, where a flashing light or audible tone may be helpful to some, it may unnerve, confuse, or even annoy others.
Stepping up the level of autonomy (or “hands-on’ness”) a notch brings us to collision preparation systems. This class of ADAS seeks to prepare for or help the driver avoid accidents. Examples include systems that pretension the seatbelts, prime the brakes, reduce the speed, or even adjust the suspension stiffness when a collision or threat appears imminent or when various sensors on the vehicle sense excessive wheel skid or roll angle.
One step closer to full vehicle autonomy lie Electronic stability control (ESC) systems, which help the driver avoid skidding and loss of control by selectively applying the brakes. This incredibly-useful (and increasingly-popular) ADAS class includes anti-lock brakes, yaw stability controllers, and roll stability controllers, among others.
Still greater levels of vehicle autonomy are found in what can be called “semi-autonomous” systems. The primary difference between these systems and the more passive stability control systems just described is that semi-autonomous hazard avoidance systems actively determine a course of action that may differ from the driver’s intended maneuver and may, when necessary, cause the vehicle to deviate from the course or speed that the driver’s commands prescribe. For example, the adaptive cruise control systems that since their 2006 debut in the United States have become an icon for intelligent (and expensive) high-end vehicles, determine based on the relative speed between the host vehicle (itself) and a hazard vehicle (the guy in front of you) whether, when, and how much to adjust velocity to avoid a collision. Another semi-autonomous ADAS that exists in very limited form today is the lane-keeping system. As its name implies, a lane-keeping system actively seeks to keep the vehicle within its current lane by applying anything from steering torque overlays to differential braking commands.
At the far end of the vehicle autonomy spectrum lie the autonomous systems. The more technical/nerdy among you may have heard or read about these during the DARPA Urban Challenge, and DARPA Grand Challenge competitions. Or, you might remember Nightrider. Autonomous systems are designed to navigate a vehicle without any human input. The common approach is to plan a path through the environment given sensory information about the location and velocity of obstacles, then track that path using a suitable controller.
At the heart of each of these sytems lies the need to determine, based on the current state of the vehicle, the environment, and (optionally) the driver, the level of threat the current situation poses to the vehicle. The algorithms used to make this evaluation are known as threat assessors and can be argued to be perhaps the single most important component of any active driver assistance system. Without an accurate assessment of threat, these systems can be ineffective at best (imagine a collision warning sound incessantly and unnecessarily dinging as you drive) and downright deadly at worst (as when a lane keeping controller misreads a lane marking and sends the vehicle careening off the road). Figure 1 illustrates the relationship between threat assessors and ADAS systems.
Given the paramount importance of an accurate threat assessment, it becomes the task of any designer to determine what exactly constitutes “threat” and how to combine various sources of threat in some meaningful way. For example, imagine a vehicle traveling down an urban road amidst other vehicles. Potentially, any vehicle up ahead could pose a threat to the host vehicle were it to slow, stop, or lose control. Similar arguments can be made for other obstacles such as pedestrians, bikers, pets, curbs, etc. How, then, does one evaluate the threat each of these obstacles poses to the vehicle? Furthermore, how does s/he combine the threat posed by various (and often very distinct) sources into a single metric upon which s/he can base the decision as to how to best assist the driver? Further complicating this task is the requirement that not only must the vehicle avoid colliding with obstacles, but it must also stay within its own lane (or whichever lane the driver chooses), remain on the road, and avoid skidding, rolling, or otherwise losing control.
Though these questions may sound philosophical, they have significant practical implications for an intelligent assistance system, which must determine whether, when, to what degree, and in exactly what manner to intervene to help the driver simultaneously avoid collisions, instability (skidding, rollover, etc.), and loss of control. In my next post, I will discuss how existing ADAS systems assess threat and seek to assist the human driver and describe how these approaches fall short of the ultimate goal of comprehensive advanced driver assistance. I will then describe the alternative: our threat assessment and semi-autonomous control system.]]>
So, is the electrical grid broken? Arguably (which I will not do here), no.
Is there a strong desire to upgrade the system so it operates better and more efficiently? Yes.
The so-called “smart grid” is a hot topic now, with a major influx of investment coming from the 2009 American Recovery and Reinvestment Act’s Smart Grid Investment Grants. However, the grid will not change overnight. In my mind, upgrading the largest “machine” in the world will be a continuous evolutionary process.
For me, this is the connection to the Design Impact blog, and I’d like to thank Dr. James Allison for inviting me to write a guest entry about the smart grid. The smart grid will ultimately have many levels of design. How should we design the smart grid? How should we design the consumer products that will interact with the smart grid? How do we design the evolution to the smart grid while continuing to operate the grid in whatever state it is currently in? With apologies to whoever said this originally (as I have forgotten), an analogy I particularly like is that upgrading the current grid to the smart grid on the fly is effectively equivalent to changing the engine on a commercial jet while it’s flying.
Designing and managing the smart grid evolution will be a huge challenge, although not insurmountable. Ultimately, designing the underlying enabling infrastructure for the smart grid will be key. At the moment, we simply aren’t sure which technologies or systems will work best for the smart grid. To address this, I am a firm believer in experimenting and trying new technologies in demonstration projects, which is precisely the point made recently by Patricia Hoffman, DOE’s assistant secretary for electricity delivery and reliability. The Smart Grid Investment Grants are certainly a solid start at funding some experimental smart grid designs. Some ideas will work, some won’t. As these demonstration projects progress, there will be a desire to keep what works and jettison what doesn’t on the fly, meaning that the smart grid will always be in a state of transition. So, how do we design the smart grid to continuously operate under continuous change?
I return to my point earlier that the underlying enabling infrastructure will be key. One effort to help support this goal is the monumental task being spearheaded by NIST to establish communication standards for the smart grid. Among other things, smart meters, utility energy management systems, home energy management systems, and even appliances will need to be able to ‘talk’ with one another. The full spectrum of devices that will connect to the smart grid will almost certainly come from more than one manufacturer, much like a multitude of devices connects seamlessly to the Internet. Establishing communication and interoperability standards is thus critically important for innovation to flourish on the smart grid just as it has on the Internet.
Smart meters are also undoubtedly a key enabling piece of the smart grid’s evolution. Electricity usage is read off of older meters at a frequency of at most once a month, whereas these smart meters will be read on the order of a few minutes to hourly. With this more frequent feedback of electricity usage, electricity customers will have a better understanding of how much electricity they use and at what times they use it. However, smart meters are just a starting point, and as a few utilities have found out, there will be some growing pains along the way as we transition into the smart grid.
These growing pains are likely part of what was behind a recent announcement that had the smart grid world buzzing: the Maryland Public Service Commission (PSC) turned down Baltimore Gas and Electricity’s smart meter rollout proposal. Personally, I think the Maryland PSC made the right call for reasons along the lines of what Chris King discusses in an article for SmartGridNews (which is a smart grid newsletter that I recommend perusing for anyone interested in easy reading and quick introductions to the many movers and shakers in the smart grid space). It’s not that the Maryland PSC doesn’t support the smart grid. Quite the opposite, I believe. My interpretation of their reasoning is simply that ‘we like where you’re going, but we think your smart grid system design should be better.’ Designing these systems is, frankly, going to be hard. Some pieces, like smart meters, are necessary enablers of the smart grid, but there is much more to truly make the system work. There are many questions to answer as well. Among them, how will customers react in the long run to smart meters, real-time electricity information and possibly time-varying pricing? Will the new smart grid system truly operate more efficiently than the old system? Again, one of the best ways to find this out in my mind is to try out some ideas through demonstration projects, just as Patricia Hoffman suggested.
I’ll stop here for this entry and return at a later date with some thoughts on one or more of the other pieces of the smart grid. I welcome any comments, questions or suggestions of which topic or topics to discuss next.
Once again, many thanks to Dr. James Allison for providing me the opportunity to write this guest entry for his Design Impact blog. Have a great day, everyone!
In hindsight the interaction between the sideways bridge motion and how people walk is clear, but it eluded engineers until it was too late.
Now take a moment and consider what we know about natural systems. They are resilient, elegant, and essential to human survival. We have studied the natural world and have remarkable (but incomplete) knowledge of it. As with engineering systems, we might have reasonable component-level knowledge, but our comprehension of the intricate inter-dependencies within natural systems is truly embryonic. Lack of system-level knowledge hinders our ability to predict the full consequences of human influences. We were caught off-guard by the results of a single interaction in the Millenium Bridge system - something that we built! What then can we expect when we mess with systems that we did not create, systems with structure only partially revealed through our observation and study?
Humans have several advantages when it comes to understanding engineered systems. We made them and know how they are put together. We can consult specifications and computer models used in their design. In contrast, we don’t have access to design plans for sophisticated natural systems that have evolved and adapted over millennia. We are constantly discovering new relationships and behavior, as well as the importance of seemingly insignificant species in ecosystems. As John Muir once said, “When we try to pick out anything by itself, we find it hitched to everything else in the universe.” The intricate links between elements of the natural world are astounding and humbling, surpassing by magnitudes the complexity of mankind’s most sophisticated creations. We can understand and predict correctly the effect of some disturbances on natural systems, but the full ramifications of human impact are likely to be more extensive and deeper than we expect — far more surprising than the wobbly bridge.
Even with modern analysis tools, predicting the results of substantial changes in engineered systems is somewhere between hard and impossible. To avoid unpleasant surprises when designing especially complex systems (automotive design, for example), engineers typically put forward designs that are essentially small perturbations of previously proven systems. We are conservative and resist ambitious changes in engineered systems, yet for some reason (economic externalities?) humans are quick to risk big impacts (pollution, unsustainable resource depletion) on the natural systems we depend on. Some dismiss the notion that humans can have extensive impact, even labeling this idea as arrogant. This convenient rationalization for continued consumption growth is short-sighted and blind to history. Human disruption has caused collapse of ecosystems, even whole societies. While past collapses have been regional in scope, modern society is more populous, resource intensive, and globally interdependent than ever, enhancing our potential for impact.
In summary, we need to recognize the limits of our ability to predict the consequences of human disruption; these consequences are likely to be more profound than we expect. Our interest in the long-term health of natural resources and ecosystems provides incentive to be conservative in our consumption and impact. Our current trajectory cannot be maintained; no system can keep expanding without bumping into limits. Planning and self-imposed restraint are more pleasant options than waiting until we run up against hard constraints such as resource depletion. As the most intelligent and powerful earthly inhabitants, stewardship to preserve is ours. Over the last year Design Impact has addressed ways to leverage our intelligence to provide a high quality of life without applying unsustainable pressure on our world, and will continue to explore how we can create a brighter future for ourselves.]]>
We have an opportunity tomorrow to learn from an impressive array of speakers at the MathWorks Virtual Energy Conference. Anyone can register (free) to watch and listen to the speakers, or to network with other participants. Many of you have probably already heard of or participated in virtual conferences, an emerging trend. If not, the basic idea is to capture many of the benefits of attending a conference in person, but via a virtual environment. You can participate from your home or office computer. I hope to see you at tomorrow’s conference!]]>
While I’m certainly an advocate of encouraging more students to consider engineering as a profession, I’m especially interested in e-week as an opportunity for the public to learn what engineering is about: what engineering has done in the past to help humanity, and the potential it has to address some of society’s most pressing present challenges. In fact, emphasis on the role of engineering in society could stimulate more interest in engineering as an attractive career choice. Senator Ted Kaufman (D-Del.), the only engineer in the Senate, explained recently that one of the road blocks in encouraging more students to pursue science and engineering careers is that they “don’t view engineering and science as the way to make a difference”, but then points out several critical issues that depend on a strong engineering workforce, including energy and economic recovery.
A clear theme throughout Design Impact articles is the positive impact engineers have on humanity. What do you see as the most important issues today that call for engineering solutions? How can we communicate best to students that a career in engineering is an opportunity to make an important difference?]]>