Adaptive cruise controllers are one of the main components of intelligent driving systems. Whereas traditional cruise control systems can regulate the speed of a vehicle at a desired level, an Adaptive Cruise Control (ACC) system is also capable of ensuring a safe following distance to (i.e., to avoid a collision with) a preceding vehicle by slowing down when necessary. Partial ACC systems are operative only in certain speed ranges and require driver intervention when the vehicle speed is outside the predefined range. Full-range ACC systems are able to control the vehicle at the full speed-range of the vehicle and can even bring the vehicle to a full stop when there is an immediate collision risk with a preceding vehicle.
Because ACC has the most control over the distance to the preceding vehicle, the focus in the art has been on front-end collisions. Early work on automatic vehicle following has focused on the technical challenge of combining throttle and brake controllers for a unified action. Fixed-gain, gain-scheduled, and adaptive PID throttle controllers as well as linear feed-back brake control and a switching logic between throttle and brake controllers have all been proposed. Similar switching logic based on sliding mode control has also been proposed for switching between throttle and braking controllers. Although these approaches show the feasibility of building controllers capable of switching between throttle and braking as needed, passenger comfort, safety, and fuel economy are not the main focus shaping the control.
In order to increase passenger comfort and better approximate real human driving characteristics, fuzzy-logic approaches have also been proposed for systems with stop-and-go capabilities, resulting in smooth transitions between throttle and brake controllers in real-world experiments with an automatically driven vehicle following a manual-driven vehicle. In order to couple comfort during normal operation but safety in rare but extreme scenarios, a linear quadratic regulator (LQR) based full-range adaptive cruise control with collision avoidance capabilities has been proposed that divides driving into “comfort,” “large deceleration” and “severe braking” modes. The LQR controllers tuned differently for each mode, and special logic is used to switch from one mode to another—effectively reducing the priority on comfort as the situation requires.
One approach to develop ACC systems with collision avoidance is to utilize vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. Variation in vehicle characteristics is possible so long as conservative bounds on vehicle acceleration and deceleration for over all vehicles are available. In another approach, linear MPC incorporating V2V and V2I communication with the target of minimizing fuel consumption uses piecewise quadratic approximation of a nonlinear static fuel consumption map of a diesel engine in order to reduce the nonlinear problem into a linear MPC problem. So far, these distributed approaches either require strong assumptions on vehicle diversity (a distributed ACC system has been shown to be verifiably safe only under the assumption that all vehicles are driven on the highway with homogeneous controllers) or require linear approximations of fundamentally nonlinear phenomena in order to make the analysis analytically tractable.
The complexity of these and other existing ACC systems belie the fact that determining the appropriate safe following distance in an ACC system is non-trivial. While studies have shown that a system of vehicles with ACC could, in principle, completely eliminate collisions, their methodologies assume either homogeneous vehicles or bounding approximations of vehicles that are so conservative that the large safe following distances betray the promise of high-capacity intelligent transportation systems. Safe and effective ACC and collision-avoidance systems require choices of following distance that are sensitive to human factors and chosen appropriately for different drivers. Moreover, human drivers are responsive to the current state and driver behavior of both the preceding vehicle as well as any following vehicles (e.g., “tail-gating behavior”). An effective ACC should similarly be sensitive to all surrounding vehicles and be shaped by safety measures that consider risk of a wide range of possible collisions while also maintaining comfort, fuel economy, and adherence to owner preferences.
Various measures for safety assessment of driving situations have been proposed in the literature. These safety assessment measures are useful for both evaluating driving safety of a controller and for design of controllers by incorporating them in the control computations. Time-to-Collision (TTC) is one of the most commonly used metrics and measures the time required for a collision between two vehicles assuming that they continue with constant velocities. A limitation of TTC is that two scenarios that both would not produce a collision under constant velocities cannot be compared using TTC although one scenario may be intrinsically safer than another. A related issue is that TTC is calculated instantaneously and can change dramatically over short time periods. Extensions to TTC have been proposed to mitigate these issues. For example, Time-Exposed TTC (TET) measures to total time the TTC value is positive and smaller than a critical value TTC*, and Time-Integrated TTC (TIT) measures the integral of TTC values over the time when TTC is positive and smaller than TTC*. However, these metrics are still focused on scenarios for which collisions are feasible in the future for constant-velocity scenarios, which is often not the case in steady-state traffic flow on highways.
When anti-lock braking systems (ABS) were first introduced, vehicle collisions temporarily increased in frequency due to legacy vehicles colliding into the rear ends of ABS-equipped vehicles with superior braking performance. This negative, albeit ephemeral, result was not anticipated because control engineers had failed to consider the effect of the following vehicle on safety. Similarly, empirical results have shown that although ACC systems decrease the collision probability of an ACC equipped vehicle with a preceding vehicle, they may increase the collision probability of a follower vehicle with the equipped vehicle. Whereas ABS is simply meant to complement a driver's own decision making, ACC systems are themselves decision-making systems. Consequently, there is an opportunity to develop ACC systems to be sensitive to both the dangers of front-end as well as rear-end collisions. To do so, safety measures have to be developed that quantify the risk of both kinds of collisions.