Valeo bends light for safety

Valeo's Bending Light system can be controlled electronically using signals from steering-wheel and wheel-speed sensors, as well as a satellite navigation system (GPS). Road curve illumination is shown with (right) and without (left) Valeo's Bending Light system.

Valeo is developing headlight technology it calls "Bending Light" that automatically directs light into road bends to optimize forward nighttime visibility. The technology is said to make a significant contribution to comfort and convenience by reducing driver stress and fatigue often associated with difficult nighttime driving conditions. It underlines the "priority Valeo places on customer-focused R&D and contributes to our goal of being recognized as the world leader in vehicle lighting technology," said Thierry Morin, Chairman of Valeo's Management Board.

The Bending Light system consists of a bi-xenon projector or reflector headlamp that can rotate up to 20° from its normal position, or an additional projector or reflector, or a combination of the two, to deliver more light into a road bend. The actuation of the motorized lighting unit within each headlamp assembly is controlled electronically by a Valeo control algorithm that employs signals from steering-wheel and wheel-speed sensors, and (optionally) a satellite navigation system (GPS). The system's specification is flexible enough to accommodate the design criteria of various vehicle OEMs.

Bending Light is the first of a new generation of adaptive front lighting systems to be launched by Valeo following an extensive R&D program. The range currently being previewed to select vehicle manufacturers includes three distinct lighting types. For Motorway Lighting, typically above 80 km/h (50 mph), the low-beam function of the headlamp is raised using a signal received from the wheel-speed sensor to actuate a self-leveling system, which increases driver visibility at high speeds. Under reduced-visibility conditions in fog, rain, and snow, Adverse Weather Lighting provides additional illumination to help keep track of road edges, while light is removed from the foreground to reduce reflection from the wet road. In well-illuminated urban zones, the Town Lighting light beam is lowered and lateral light is increased, improving pedestrian and cyclist identification at crossings and reducing "dazzle."

- Kevin Jost

Bosch pre-crash sensing for airbag deployment

Robert Bosch's pre-crash sensing system uses one or more radar sensors.

Developers of modern airbag systems have to design control units that provide multi-stage deployment at exactly the right time. This goal is difficult when acceleration-sensing triggers are mounted inside the passenger compartment on the transmission tunnel. However, the use of pre-crash sensing to measure relative velocity of other vehicles can improve airbag safety. Research has confirmed that knowledge about relative speed would be useful in more than 80% of light-duty vehicle crashes in the U.S.

A system presented by Robert Bosch researchers at the SAE 2002 World Congress uses one or more radar sensors for pre-crash sensing. It not only measures deceleration using a central onboard sensor, but it also watches the area in front of the car to detect an oncoming crash situation. With this information, time-to-impact and relative closing velocity can be determined. Additionally, the system differentiates between crash and misuse situations, while determining requirements for low-risk deployments with high accuracy. Lastly, time-to-impact information allows activation of reversible restraint systems and a special occupant detection system in the period just before the collision.

The system's algorithm must make the deployment decision and then calculate deployment time from the closing velocity and acceleration signal. The processing method is based on the identification of detailed features contained in the signals.

Key features have been extracted from the acceleration signals of crash tests. Then, using physical knowledge of crash progression, these details are generalized to real-world situations. In this way, the crash can be classified, allowing its assignment to a particular deployment characteristic. Knowing the crash's characteristic, time-to-fire can be computed for each deployment class.

With two pre-crash signals of acceleration and closing velocity, the physical interpretation of the algorithm becomes easier and the structure of the algorithm more obvious, according to researchers. The basic feature of the algorithm for the deployment decision is the second integral of the acceleration signal. For a better identification of frontal crashes into a rigid barrier, a gradient-based feature was used. Future investigation will determine if the two-gradient approach can be extended to the entire crash sequence. In that case, an additional feature would need development to identify different crash classes. Computed deployment times could be improved in the future by using additional parameters to determine deployment.

- John Fobian