Lateral Collision Avoidance  


INTRODUCTION

This technology addresses collisions caused when a vehicle leaves its lane in a lateral maneuver, either running off the road and losing control and crashing into a roadside object or crashing into an oncoming vehicle either head-on or, in the case of vehicles traveling in the same direction, in a sideswipe or merge crash.

Typically, driver behavior falls into one of two categories: either the driver was paying attention and misjudged the task, or the driver was not paying attention because of distraction or impairment. The driver's role in the crash will influence the design and function of a collision avoidance technology, as will the type of crash. Typical lateral collisions and the driver behaviors that are associated with them are summarized below in Table 1.

Lane departures are a major component of the National Highway Traffic Safety Administration's research program for collision avoidance. The other areas of research are technologies to prevent longitudinal collisions and intersection collisions, which are addressed in separate sections (see sidebar).

Table 1. Driver Behaviors and Associated Crash Types
  Crash Type
Driver Behavior Sideswipe Head-on Run-Off Road
Driver Misjudgment
  Merging X    
  Passing Vehicles X X  
  Negotiating Curves X X X
Driver Inattention
  Negotiating Curves X X X
  Sideways Lane Departure X X X
  Crossing Centerline/ Divider   X X

RATIONALE

Lateral collisions typically result in more severe injuries and higher fatalities, according to the two major crash databases, the Fatal Analysis Reporting System (FARS) and the General Estimates System (GES). Run-off-road crashes, for example, account for approximately 15% of all crashes reported in FARS, but they make up more than two times their share (approximately one-third) of all fatal crashes. Crashes where the vehicle failed to negotiate a curve were 4.7% of total crashes, but roughly three times that share, some 14%, of fatal crashes. By contrast, crashes from intentional lane departure maneuvers, while 3.7% of the total, were only 1.7% of fatal crashes, and left-turning crashes were 10.8% of the total, but significantly lower percent, 6%, of the fatal. Head-on crashes made up about 2% of all police reported crashes, but they comprised about five times that share, 10%, of all fatal crashes.

Roughly two-thirds of all lane change or merge crashes occurred under good environmental conditions, suggesting that road conditions were not a contributing factor and that driver misjudgment or inattention was the likely cause. In crashes where driver inattention was identified as a contributing factor, three major forms emerged: distraction from the driving task, failure to process information (e.g., looked but did not "see"), and drowsiness or sleep. Preliminary studies have shown that these were behaviors that could be modified by collision avoidance technologies.

A study by the National Traffic Safety Board (NTSB) targeting six types of crashes showed that drivers did not attempt an avoidance maneuver in a majority of the lane and roadway departure crashes. This suggests that they were unaware of the danger of the maneuver. This could be corrected by a warning or assistive system. The crashes analyzed in this study included: rear end, backing, single vehicle roadway departure, lane change/merge and signalized intersection/crossing path.

SYSTEM DESCRIPTION

Principles of Operation

Collision avoidance technologies can be divided into systems that are progressively more intrusive, starting with those that merely warn or advise the driver based on sensors inside or outside the vehicle. Next are those that take over partial control of the vehicle or make safety-enhancing adjustments such as tightening seatbelts, turning down the throttle, activating the brakes and changing the suspension without the driver taking action. The third type takes over full control of the vehicle.

Collision warning systems act on the principle that enabling drivers to accurately recognize their environment will enhance their safety. They prompt the driver with an alert when danger exists during a lane change or when the vehicle is having difficulties in lane-keeping. An additional category of collision warning particularly applicable to run-off-road crashes is driver monitoring, which detects and warns of drowsiness or other impairments that may prevent the driver from safely operating the vehicle. These are being deployed most widely in trucks, because of the high costs of fatigue- and drowsiness-related crashes.

Driver assistance systems are more complicated in nature and involve a combination of roadway detection technology and in-vehicle mechanical systems to properly assess driver judgment and correct for driver error and inattention. Longitudinal Collision Assistive technologies to prevent longitudinal collisions are the most advanced, with the development of systems such as adaptive cruise control and automatic braking systems. Driver assistance systems should noticeably reduce errors in recognition and judgment, without taking away the greater part of the driving responsibilities from the driver, as a purely automated system would.

Automated vehicles are typically part of a cooperative system linking the vehicle to the infrastructure through sensors and guidance systems. Automated buses that use precision docking are examples of that kind of application and do not fall under the purview of collision avoidance systems being discussed in this paper. An ITS Decision report on Longitudinal Collision Avoidance can be found here.

Technologies Used

The main components of any Lateral Collision Avoidance System are the sensing mechanism used for monitoring lane position and the driver interface that issues the warnings.

A number of basic sensor technologies have been investigated for the detection of lane boundaries or lane center and the vehicle's lateral position relative to the road. The most promising ones appear to be:

  • Vision-based systems, especially digital video, for lane drift warnings
  • Radar based systems for side-object detection and curve speed warnings
  • Machine vision applications, which use image-based algorithms to generate vehicle trajectories in relation to the roadway or other vehicles, or probabilistic grouping algorithms for lane detection

There are also a limited number of infrastructure-based systems for speed management that use embedded loop detectors to determine speed and interactive variable message signs to display appropriate warning messages.


RESEARCH

The most significant research direction for lateral collision avoidance appears to be curve speed warning systems, which trigger an alarm when a vehicle is approaching a curve at a speed that the system determines to be too fast.

In 1999, the National Highway Traffic Safety Administration (NHTSA) sponsored the Run-Off-Road Collision Avoidance Program, which was instituted to determine and provide performance guidelines for the operation of a lane drift warning system and curve speed warning system with a goal of minimizing nuisance and false alarms. The team was made up of researchers from Carnegie Mellon University, AssistWare Technology, Battelle Memorial Institute and the University of Iowa. They developed guidelines for a camera-based system for sensing the position of the vehicle in its lane in order to provide lane-drift warnings. At the time of the research, there was a need for more accurate digital maps, and better sensing of pavement conditions to make curve speed warning systems feasible.

In 2001, in the Run-Off-Road Crash Prevention Project, a team of researchers from Visteon, AssistWare Technology, and the University of Michigan augmented and extended the 1999 research by adding radar to enable the system to detect nearby objects in adjacent lanes as well as measure drift and weaving. The technology warns the driver of imminent collision, but does not take control of the vehicle at any time.

ASSESSMENT

The system works on straight and curved roads, in daytime and nighttime and under certain inclement weather conditions, such as light rain. However, during more inclement conditions, such as a heavy rain at night, the system is not as fully functional. When conditions interfere with full functioning, the system alerts the driver that it is not scanning. It turns itself back on when conditions become favorable.

BENEFITS

Collision Avoidance Technologies could save lives and money. Also, by helping to correct some common driver behaviors that may not always lead to a crash, they could create safer driving conditions overall, not just for the vehicles in which they are deployed, but for the vehicles in their vicinity.

COSTS

Because infrastructure changes are rarely needed for systems that address this type of collision, this is not a societal program; rather it is focused primarily on technology for private vehicles. As a result, costs are internal and are between Original Equipment Manufacturers and consumers.

IMPLEMENTATION CHALLENGES

  • Costs of adoption: because most systems are being offered as options on new vehicles, broader implementation will require investment in replacing current automotive and truck fleets.
  • Liability and operability issues due to false alarms and driver over-reliance on alarms or assistive systems.

Implementation

Visteon

Visteon is quoting lane departure warning and side-object detection systems to OEMs for possible incorporation into vehicles selling 24-36 months hence.

Visteon has been developing the smart radar "cocoon" which surrounds the vehicle with programmable sense zones that are used for adaptive cruise control, side-object warning and a lane change aid.

SafeTRAC

A commercial product that grew out of the 1999 Run-Off-Road Study, the SafeTRAC system uses a video camera to monitor the road and provides an auditory warning if the vehicle begins to drift off the road or to weave excessively.

The system is comprised of a windshield mounted camera and a driver interface which attaches to the vehicle and is powered by the cigarette lighter. It has been commercially available since early 2000, but has not been widely adopted. SafeTRAC is currently used in GM/NHTSA collision avoidance program for lane tracking. It is available as a factory option in Kenworth Trucks and Volvo is also using it in the US Army’s 21st century truck.

AutoVue

AutoVue has been installed by Mercedes in its trucks since 2000 and is a factory-installed option on a number of North American truck models. The system consists of a camera and computer processor that attaches to the dashboard or windshield. The camera sends a continuous feed of roadway images to the processor, which tracks lane markings and the vehicle's speed. The processor feeds that data into an algorithm to determine if the vehicle is at risk of drifting out of its lane or running off the road. It activates an alarm that makes the sound of rumble strips if it determines that the vehicle is drifting out of its lane. To reduce false alarms, the warning sound is muted when the driver uses the turn signal.

AutoVue has been shown to work effectively in both day and nighttime and most weather conditions where lane markings are visible. Other available features exist to turn headlamps on and off and activate windshield wipers when conditions merit.

Additional Private Industry Development

Additional programs for vehicle-based systems have been implemented in Japan with Nissan’s "lane guide" lane keeping steering control system and Japan’s Smart Cruise 21 program consisting of "Support for Prevention of Lane Departure" systems demonstrated in November 2000. It will be available in the 2005 Infiniti M45 shown at the Tokyo Auto show in 2003. The development team was made up of: Honda, Isuzu, Mazda, Mitsubishi, Nissan, and Toyota.

Infrastructure-Based Systems

Infrastructure based systems have been rarely deployed, but one has been active west of Denver, Colorado on a rural section of I-70 outside the Eisenhower Tunnel. It is used for speed management and reduction of runaway truck crashes. It is an interactive system that tracks vehicles with inductive loops and Piezo weigh-in-motion sensors in two lanes. The data they gather is used to trigger appropriate warnings on variable message signs tailored to each vehicle. Outputs are updated continuously and include: recommended speed gearing and steep grade warnings. Since the system was deployed, truck-related crashes on the steep downhill sections have declined, while the volume of truck traffic has increased by an average of 5 percent per year. The average truck speed in the downgrade area has decreased, and response from truck drivers has been positive.

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Author: Terri O'Connor

April 2004

Last updated April 15, 2004